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From Static Compliance to Living Compliance: How Agentic AI Can Make Health Care Operations Safer

christina.reilly@829llc.com — Wed, 29 Apr 2026 17:50:05 +0000

This piece was written in collaboration with Sam De Brouwer, co‑founder and CEO of , and Lamara de Brouwer, co‑founder and CTO of .

Executive Summary

Health care compliance today is manual, retrospective, and brittle. Humans are expected to remember rules, document decisions, and reconstruct context months later during audits. The result is a system that doesn’t scale — one where patient safety, operational efficiency, and regulatory defensibility are perpetually at risk.

Agentic AI offers a fundamentally different approach. When designed with deterministic execution, constrained autonomy, and human-in-the-loop oversight, these systems enable continuous, auditable, real-time compliance. The result is not less control, but more.

This paper presents a joint legal and technical perspective on how health care organizations can transform compliance from a periodic burden into an always-on operational advantage — without venturing into clinical decision-making or creating new liability exposure.

1. The Compliance Reality Today

Walk into any health care organization, and you’ll find the same pattern: compliance lives in binders, spreadsheets, and the institutional memory of overworked staff. Regulatory requirements from HIPAA, CMS, state licensing boards, and payer contracts create a web of obligations that must be tracked, documented, and proven during audits that can occur months or years after the fact.

The fundamental problem is that human memory serves as the primary control layer. Staff must remember which forms require signatures, which authorizations need renewal, which coding guidelines changed last quarter, and which payer requires which documentation. When they forget — and they inevitably do — organizations face denied claims, audit findings, regulatory penalties, and in the worst cases, patient harm.

Current systems are fragmented by design. Electronic health records handle clinical documentation. Practice management systems handle billing. Separate platforms manage credentialing, contracting, and quality reporting. Each system maintains its own version of truth and reconciling them requires manual effort that rarely happens until an auditor demands it.

The result is retrospective compliance — organizations discover problems only when claims are denied, audits are scheduled, or regulators come calling. By then, the context that would explain decisions has evaporated, the staff who made those decisions may have moved on, and reconstruction becomes an expensive forensic exercise.

2. What Changes with Agentic AI?

Agentic AI represents a category shift from the chatbots and predictive analytics that have characterized health care’s AI adoption to date. Where traditional AI systems respond to queries or flag patterns, agentic systems act: they pursue goals, execute workflows, and interact with other systems — all within defined boundaries.

The distinction matters for compliance. A chatbot can tell a biller that a claim might be denied. An agentic system can validate that claim against payer requirements before submission, flag specific deficiencies, gather missing documentation, and either route for human review or proceed based on pre-defined rules. The compliance check becomes embedded in the workflow rather than layered on top of it.

This is what we mean by “compliance by design.” Instead of writing policies that humans must remember to follow, organizations encode those policies into executable logic that agents enforce automatically. The question shifts from “Did staff follow the policy?” to “Is the system configured correctly?” — a question that can be answered definitively and audited systematically.

Critically, effective agentic AI for compliance requires three architectural commitments: deterministic execution (the same inputs produce the same outputs), constrained autonomy (agents operate only within defined boundaries), and human-in-the-loop oversight (humans retain authority over consequential decisions). Without these, organizations simply trade one set of risks for another.

3. Safety, Accuracy, and Accountability

Health care leaders approaching agentic AI consistently raise three questions: What happens when the AI is wrong? Who is accountable? Can we explain this in an audit? These questions deserve serious answers, not dismissive assurances.

Deterministic vs. Probabilistic Systems

Large language models generate responses probabilistically — the same prompt can produce different outputs. This creates obvious problems for compliance, where consistency and predictability are paramount. Deterministic agentic systems address this by separating natural language understanding (which may use probabilistic models) from execution logic (which follows defined rules). The language model interprets the request; the execution engine performs the action. This architecture makes behavior predictable and testable.

Human-in-the-Loop Governance

Staged autonomy addresses the accountability question. For low-risk, high-volume tasks (verifying that a form is signed), agents can act autonomously. For higher-stakes decisions (submitting a complex claim, escalating a denial), agents surface recommendations for human approval. The threshold for autonomy becomes a policy decision that organizations can calibrate based on their risk tolerance and regulatory requirements. Humans remain in control; agents handle the mechanical burden.

Explainability and Replay

For audit defensibility, every agent action must be logged with sufficient context to reconstruct why it happened. This means capturing not just the action and outcome, but the inputs that triggered it, the rules that applied, and the human authorizations in effect. When an auditor asks, “Why was this claim submitted this way,” the organization should be able to replay the exact decision sequence rather than relying on someone’s recollection.

4. From Policies to Systems

The most profound shift that agentic AI enables is the transformation of compliance from documentation to infrastructure. This section explains what that transformation looks like in practice.

Policies become executable logic. Consider a payer contract that requires prior authorization for certain procedures. In traditional compliance, this policy exists as a document that staff must remember to consult. In living compliance, the policy is encoded as a rule that the system evaluates automatically: when a procedure code matches the authorization requirement, the system initiates the authorization workflow before scheduling can proceed. The policy enforcement is guaranteed, not hoped for.

Controls become automated checks. Manual compliance checklists — Did the patient sign the consent? Is the provider credentialed for this service? Does the documentation support the code? — become automated validations that run continuously. Deviations trigger alerts or blocks in real time rather than showing up in quarterly audits.

Audits become queries. When compliance state is maintained systematically, audit response transforms from investigation to reporting. “Show me all claims submitted without required authorization” becomes a database query that returns in seconds, not a weeks-long document review. The organization’s compliance posture becomes observable at any moment, not just during audit preparation.

5. Practical Use Cases

The principles above apply most naturally to operational workflows that are high-volume, rule-governed, and administratively burdensome — but not clinically sensitive. By focusing on operations rather than clinical decision-making, health care organizations can capture significant value while maintaining low risk and high adoption.

Revenue cycle workflows offer immediate opportunities. Agents can validate claims against payer requirements before submission, identify coding inconsistencies, manage denials by assembling required documentation automatically, and reconcile payments against expected reimbursement. Each of these tasks follows defined rules that can be encoded and executed systematically.

Prior authorization is perhaps the highest-impact application. The current prior authorization process is universally despised: providers spend hours gathering requirements, submitting requests, and tracking status across multiple payer portals. Agentic systems can verify eligibility, identify authorization requirements, assemble documentation from clinical records, submit requests, and monitor status — all while maintaining complete audit trails of every action taken.

Documentation integrity benefits from continuous monitoring. Agents can verify that required signatures are present, that documentation supports billed services, that all mandatory fields are completed, and that records maintain consistency across systems. Problems surface immediately rather than during retrospective audits.

Payer-provider data alignment addresses a chronic source of compliance failures. Agents can continuously verify that contracted rates match claim payments, that provider credentials remain current with all payers, and that network status is accurate across all platforms. Discrepancies trigger immediate investigation rather than accumulating until they become material.

The regulatory instinct when facing new technology is often to restrict until proven safe. With agentic AI in health care operations, this instinct may be counterproductive. Organizations using well-designed agentic systems will likely demonstrate better compliance than those relying on traditional manual processes. They’ll have more complete documentation, fewer errors, and faster response to requirements changes. Regulators should encourage the adoption of auditable systems by accepting system-generated compliance evidence and providing clear guidance on what constitutes acceptable automation in different contexts.

For Health Care Operators

Start with low-risk, high-burden workflows where the compliance rules are clear and the consequences of errors are financial rather than clinical. Revenue cycle and prior authorization are natural starting points. Build internal expertise by piloting with specific payers or service lines before expanding. Invest in change management: staff need to understand that agentic systems augment their capabilities rather than threaten their roles. Most importantly, insist on auditability — any system that cannot explain its actions is creating compliance risk rather than reducing it.

For Technology Builders

The temptation in AI development is to maximize capability. In health care compliance, the imperative is to maximize trustworthiness. This means separating language understanding from execution logic, maintaining deterministic behavior for all compliance-critical functions, building comprehensive audit trails, and designing for staged autonomy that keeps humans in control of consequential decisions. Token-heavy black-box approaches may be technically impressive but are fundamentally unsuitable for environments where explainability and consistency are requirements, not preferences.

Conclusion

Health care compliance doesn’t have to be a periodic scramble driven by audit calendars and institutional anxiety. Agentic AI, when designed with appropriate constraints and controls, can transform compliance into continuous, observable, and reliable infrastructure—reducing administrative burden, improving accuracy, and creating defensible records that serve organizations well when regulators come calling.

The technology is ready. The regulatory environment is receptive. The operational pain is acute. What remains is for health care leaders, technology builders, and legal advisors to work together in designing implementations that capture the benefits while managing the risks. This paper represents our commitment to that collaboration.

Compliance stops being a document. It becomes a system.

About the Authors

Natasha Allen is a partner at �鶹ֱ�� & Lardner LLP, and chairs its AI sector, specializing in health care regulatory compliance and operational risk management. She works with health systems, physician groups, and health care technology companies on compliance program design and regulatory strategy.

Sam de Brouwer is co-founder and CEO of (XYCorp Ltd), building agentic AI infrastructure for health care operations. Her work focuses on deterministic execution architectures while continuously learning that enable enterprise-grade automation with full auditability.

Lamara de Brouwer is co-founder and CTO of (XYCorp Ltd), where he leads engineering. He brings expertise in translating operational complexity into systematic, auditable processes.

Louis Lehot is a partner at �鶹ֱ�� & Lardner LLP, where he advises companies at the intersection of health care and technology on formation, financing, scaling, governance and exit planning. He has counseled numerous frontier-tech organizations on AI implementation strategies and regulatory frameworks.

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GLP-1 Receptor Agonists: The Surge of M&A Activity and the Future of Metabolic Health

bklemm@foley.com — Tue, 03 Jun 2025 19:59:13 +0000

Over the past decade, the global pharmaceutical industry has witnessed a transformation in the treatment landscape for metabolic disorders, particularly type 2 diabetes and obesity. At the heart of this shift is the explosive rise of GLP-1 receptor agonists, once a niche therapeutic class, now a multi-billion-dollar market shaping the future of chronic disease management. With this surge in clinical and commercial success, the GLP-1 technology space has become a hotbed for mergers and acquisitions (M&A), as major pharma players race to secure their foothold in what many consider the next blockbuster category.

This post explores the strategic drivers, recent deals, and future implications of M&A activity in the GLP-1 sector, unpacking why this field has become a focal point for investment, innovation, and consolidation.

GLP-1: From Diabetes Therapy to Weight Loss Powerhouse

Originally developed to help manage blood sugar in type 2 diabetes, GLP-1 receptor agonists have evolved beyond their initial indications. These drugs mimic the glucagon-like peptide-1 hormone, which enhances insulin secretion, suppresses glucagon, and slows gastric emptying. What made them revolutionary was their ability to induce weight loss, a feature that has become a cornerstone in the battle against obesity, a condition with vast medical, social, and economic implications.

The commercial success of drugs like Novo Nordisk’s semaglutide (Ozempic^®, Wegovy^®) and Eli Lilly’s tirzepatide (Mounjaro^®, Zepbound^®) has catalyzed not just clinical excitement but also intense investor and acquirer interest. These medications have demonstrated not only weight loss upwards of 15-20% but also cardiovascular and metabolic benefits, positioning them as game-changers in preventive medicine.

Why the GLP-1 Market Is Attracting M&A Interest

Massive Market Potential: Obesity affects over 890 million adults globally[1], with related comorbidities including diabetes, cardiovascular disease, and certain cancers. Analysts estimate the GLP-1 market could reach US$139 billion annually by 2030.[2]
Pipeline and Platform Expansion: As demand surges, big pharma companies are under pressure to expand their metabolic portfolios. M&A offers a fast-track route to diversify into GLP-1 and adjacent incretin-based therapies (e.g., GIP (Gastric Inhibitory Polypeptide), dual/triple agonists).
Technology Differentiation: Although current GLP-1s are effective, there is a premium on next-generation delivery systems (oral, transdermal, long-acting injectables) and multi-modal agonists. Acquiring innovative biotech firms with proprietary delivery platforms or novel peptide structures provides a competitive edge.
Limited In-House Innovation: Not all big pharma players developed GLP-1 assets internally. As a result, acquiring external innovation, whether through platform technologies or late-stage assets, is becoming a strategic imperative.

Recent M&A Moves in the GLP-1 Landscape

The following transactions illustrate the growing M&A momentum in the GLP-1 space:

Roche’s Acquisition of Carmot Therapeutics (2023):
Roche paid US$2.7 billion upfront for Carmot, a clinical-stage biotech with a promising portfolio of incretin-based therapies, including dual GLP-1/GIP agonists. This move positioned Roche to compete in the metabolic disease arena, leveraging Carmot’s small molecule platforms to diversify beyond oncology.[3]
AstraZeneca’s Acquisition of CinCor Pharma (2023):
Though primarily targeting hypertension, AstraZeneca’s US$1.8 billion buyout of CinCor included a nod toward combinational potential with metabolic therapies, signaling broader interest in cardiometabolic synergies.[4]
Pfizer’s Strategic Reassessment and Potential for Acquisitions (2024, 2025):
After halting internal development of its oral GLP-1 candidate due to tolerability concerns, Pfizer has publicly stated its interest in acquiring external GLP-1 assets. Although no formal deal has closed, analysts speculate an acquisition could be imminent, especially in the wake of divestitures from its COVID-era portfolio.[5]^{, [6]}
Novo Nordisk’s Expansion via Catalent Deal (2024):
Novo Nordisk’s US$16.5 billion acquisition of Catalent’s fill-finish facilities demonstrates another angle of strategic M&A, ensuring capacity for GLP-1 manufacturing and reducing supply chain risks amid skyrocketing demand.[7]

Beyond Big Pharma: Biotech’s Role as Innovation Engines

Small- to mid-cap biotech firms are proving essential in advancing the next frontier of GLP-1 therapies. These companies are often rich in innovation but resource-constrained, making them prime acquisition targets for larger players. A few noteworthy examples include:

Structure Therapeutics: Developing oral GLP-1R agonists, Structure has seen rapid investor interest post-Initial Public Offering (IPO) and is considered a prime candidate for acquisition due to its differentiated delivery approach.[8]
Terns Pharmaceuticals: The company recently announced positive Phase 1 clinical results for its oral GLP-1R agonist for treating obesity, triggering acquisition rumblings.[9]
Viking Therapeutics: The company is advancing early-stage GLP-1 or dual agonists with strong preclinical profiles and have recently experienced stock price surges on clinical data, fueling acquisition speculation.[10]

Strategic Implications and Risks

Although the M&A landscape in GLP-1 technology is vibrant, it is not without challenges, such as the following:

Supply Chain Constraints: Companies acquiring GLP-1 assets must also manage complex peptide synthesis, formulation, and delivery bottlenecks. This makes CMC (chemistry, manufacturing, controls) and scale-up capabilities a due diligence priority.
Regulatory Scrutiny: With GLP-1s being prescribed off-label for cosmetic weight loss, regulators may impose tighter restrictions, impacting long-term market forecasts.
Valuation Pressure: As competition intensifies, valuations are soaring. This poses a risk of overpaying for early-stage assets with limited clinical data. Strategic buyers must carefully weigh scientific promise against commercial risk.
Patent Life and Biosimilar Threats: The first-generation GLP-1s are already facing biosimilar competition timelines. Acquirers must assess exclusivity windows and invest in lifecycle management strategies.

The Future of M&A in the GLP-1 Arena

Looking ahead, several trends are likely to shape the next wave of M&A, including the following:

Combination Therapy Focus: Expect acquisitions targeting companies developing dual/triple agonists (GLP-1/GIP/Glucagon) to address broader metabolic endpoints.
Digital and Companion Tech Integration: Companies integrating wearables or AI-driven metabolic monitoring may become attractive M&A targets for firms aiming to offer holistic obesity/diabetes management solutions.
Emerging Markets Expansion: Firms with strong distribution in Asia, Latin America, or Africa may see M&A interest as acquirers look to expand the global footprint of GLP-1 therapies.

Conclusion

The GLP-1 revolution is not only transforming the treatment of diabetes and obesity, it is also reshaping the strategic priorities of the pharmaceutical industry. With blockbuster sales, novel delivery technologies, and growing clinical applications, GLP-1s have become a magnet for M&A. For investors, innovators, and pharma executives alike, the current M&A wave in the GLP-1 sector represents both opportunity and inflection, one that will define the metabolic health market for years to come.

As GLP-1 technology continues to evolve, one thing is certain: the race to acquire the next breakthrough is only just beginning.

For additional resources on GLP-1 Receptor Agonists and how they will change the health care & life sciences and technology industries, click here to read the other articles in our series.

�鶹ֱ�� is here to help you address the short- and long-term impacts in the wake of regulatory changes. We have the resources to help you navigate these and other important legal considerations related to business operations and industry-specific issues. Please reach out to the authors, your �鶹ֱ�� relationship partner, our Health Care & Life Sciences Sector, or to our Innovative Technology Sector with any questions.

[1] World Health Organization, Obesity and overweight, May 7, 2025; .

[2] TD Securities, GLP-1 Market: The Pipeline Expands, Mar. 10, 2025; .

[3] “Roche to acquire Carmot Therapeutics for $2.7bn,” Pharmaceutical Technology, Dec. 3, 2023; .

[4] “AstraZeneca to buy US-based CinCor Pharma for $1.8bn,” Pharmaceutical Technology, Jan 10, 2023; .

[5] A. Zank, “Pfizer CFO talks strategic growth through acquisitions,” CFO Brew, Sept. 24, 2024; .

[6] M. Lee, “Pfizer Eyes $20 Billion Revenue From Acquisitions Amid Patent Cliff Challenges,” AInvest, Jan. 14, 2025; .

[7] “Novo completes $16.5bn takeover of Catalent,” PharmaPhorum, Dec. 2024; .

[8] “Structure Therapeutics Announces First Patients Dosed in Phase 2b ACCESS Clinical Study Evaluating Oral Small Molecule GLP-1 Receptor Agonist, GSBR-1290, for Obesity,” Global Newswire, Nov. 13, 2024; .

[9] “Terns Pharmaceuticals Announces Positive Phase 1 Clinical Trial Results with TERN-601 Once-Daily Oral GLP-1R Agonist for the Treatment of Obesity,” Global Newswire, Sept. 9, 2024; .

[10] “Viking Therapeutic’s new GLP1 impresses at ObesityWeek 2024,” GLP1.Guide, Nov. 4, 2024; .

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Antibody Drug Conjugates Keep Growing: What You Need to Know

bklemm@foley.com — Wed, 11 Dec 2024 15:56:46 +0000

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Patenting Power Plays For AI Drug Discovery

bklemm@foley.com — Tue, 16 Jul 2024 14:36:00 +0000

In April 2024, BioPharmaTrend published a of the past and current state of artificial intelligence (AI) drug discovery, using the 2012 advent of modern deep learning as a starting point. The review particularly highlighted 9 leading companies in the space that “can design and advance drug candidates de novo (mostly small molecules) and have internal pipelines.”

Given the criticality of patent rights in pharmaceuticals and biotechnology and the impending patent cliff, we analyzed whether trends could be drawn or strategies could be inferred from these 9 exemplary companies’ patent portfolios[1]. This article, part one of two, poses several questions to analyze the companies’ patent strategies and offers high-level conclusions about their portfolios. Part two will dig deeper into how these portfolios are making use of analytics and tactics for effective portfolio development and provide a practical framework to develop and execute on patent strategy in this field.

Analyzing The Patent Portfolios

Here are some key questions to help understand the patent portfolios of the 9 profiled AI drug discovery companies, including how they compare with those of the “big pharma” company portfolios:

Are these companies protecting aspects of their AI/machine learning (ML) technologies in addition to conventional pharmaceutical technologies e.g., drug compositions, in vivo testing, wet lab screening, etc.?
Are there any trends over time within AI/ML vs. conventional technologies?
How well do the patent portfolios map to the publicly known targets that these companies are focused on?
How do these patent portfolios compare to the total active U.S. patents/pending applications for those same targets?

Below is a first pass evaluation of data[2] that can help answer these questions:

1. Composition vs. AI/ML Protection and Trends over Time

Table 1 provides totals patent assets that can be clearly mapped (based on keywords[3]) to protection for drug compositions or other conventional technologies vs. AI/ML or other computational-first technologies.

Table 1. Filing Landscape (all U.S. patent assets across all companies)

Company	Drug Composition/Conventional Technology Filings (% of total)	AI/ML Filings (% of total)	Total
Total	263 (67)	127 (33)	390

As shown in Table 1, the overall patent filings lean towards conventional pharmaceutical relative to AI/ML (and computational) filings. This may, however, reflect certain companies historically having more conventional filings before expanding their AI/ML presences.

2. Trends Over Time

Figure 1 provides counts of filings since 2012 in each category. Filings for these companies have generally increased in volume for both AI/ML and conventional technologies, with more of the balance towards conventional technologies. These trends may reflect both expansion of the companies’ patenting efforts and the relative new formation of these companies.

Figure 1. Filing Trends by Category

3. Portfolio -> Target Mapping

The hypothesis for these companies is that they would build their patent protection to align with the targets that they have made known to be significant: the most typical patent strategy is to align filings with key commercial priorities. Table 2 below lists the published targets along with a count of patent filings that correspond with those targets, based on keyword searches for the targets in the patent portfolios.[4][5]

Table 2. Target Mapping

Target	Conventional Filings	AI/ML Filings
3CLpro	2
A2aR	1
APJR	1
Bcr-Abl
CDC7
CDC7
CDK2	1
CDK7	1
CDKs	2	1
CHK1
cMYC
DGKA	1
EFGR(C797S)	10
ENPP1
ENPP1
��α	8
FGFR2	10
FGFR2/3	1
HDAC		1
HIF-2a
HPK1
HPK1
HSP90	7
JAK2	1
KAT6
LPA1R
LRRK2
LSD1
MALT1
MALT1
MAT2A	1
MEK1, MEK2
Mpro
MYT1
NLRP3
NLRP3
NLRP3
PARP1
PARP7
PDE10
PHD1/2	1
�ʱ�3��α	10
PIKfyve
PKC, GSK3β	1
PKC-theta	1
PRMT5-MTA
QPCTL
�鴡��αβ
RBM39	1
ROCK 1/2 inhibitor
S1P1 agonist
SHP2	10
SOS1
TEAD	1
TNIK	2
TrkA, TrkB, TrkC
TYK2	2
USP1	1
USP28	5
USP7	6
WEE1
α4β7	1
Α��β8

All but two of the AI/ML filings are silent as to their key targets. This leads to several follow-up questions/hypotheses about how these patent portfolios are being developed:

Does patent protection for AI/ML technologies need to be focused on specific targets? This might depend on how closely the targets are tied to the development of the AI/ML technologies, as opposed to the technologies being generally applicable across targets.
What other factors drive the selection of the key targets for the AI drug discovery pipeline? The companies may have data moats that are well-integrated with AI/ML technologies, or overall company mission goals tied to specific targets – even if the AI/ML technologies themselves need not be limited to those targets.
Are patent/IP resources being devoted to goals other than protection directed towards specific targets? On one hand, especially for early-stage companies, it is critical to identify priorities in IP protection and then deploy resources to protect innovations in a manner consistent with these priorities. On the other hand, resource prioritization is all the more reason to focus IP resources on protecting fundamental technologies that tie directly to companies’ commercial priorities.
Are targets a useful proxy variable to evaluate patent strategy? While targets make it easy to perform a first pass/direct comparison of patent filings and company publications, other factors to consider could include indications, drug classes, or information presented in the patent filings that explain the improvements achieved and what the AI technologies can be used for.
How does patent publication lag impact forecasting? As noted, the typical 18 month lag from patent filing to publication obscures the most cutting-edge technologies. Looking back at the BioPharmaTrend article, many of the targets identified in Table 1 did not reach move beyond the discovery phase until 2022 or later, and thus may have corresponding patent filings that are not yet public.

4. Comparing AI Company Patent Portfolios with the Overall Landscape

To help provide context for the companies’ patent portfolios, they can also be compared with the overall landscape, with targets as the proxy, to provide an order-of-magnitude assessment of the companies’ share of the overall landscape.

Table 3 lists the approximate total number of U.S. filings for those targets and for the largest filer. Given the sparsity of filings shown in Table 2, this list has been limited to comparing targets where the AI drug discovery companies have at least one filing.

Table 3. Patent Filings by Target Relative to Overall Landscape

Target	Composition	AI/ML	Approximate Total US Filings (Approximate Total for Leading Filer)
3CLpro	2		3800 (100)
A2aR	1		6500 (120)
APJR	1		1600 (19)
CDK2	1		Total indeterminate (40)
CDK7	1		4000 (100)
CDKs	2	1	Total indeterminate (170)
DGKA	1		360 (25)
EFGR(C797S)	10		Total indeterminate (380)
��α	8		10000+ (80)
FGFR2	10		Total indeterminate (40)
FGFR2/3	1		Total indeterminate (40)
HSP90	7		10000+ (60)
JAK2	1		10000+ (200)
MAT2A	1		2800 (15)
PHD1/2	1		Total indeterminate (50)
�ʱ�3��α	10		2000 (40)
PKC, GSK3β	1		Indeterminate (180)
PKC-theta	1		4000 (40)
RBM39	1		300 (20)
S1P1 agonist			Total indeterminate (80)
SHP2	10		7000 (100)
TEAD	1		Total indeterminate (10)
TNIK	2		400 (50)
TYK2	2		7400 (70)
USP1	1		3000 (60)
USP28	5		Total indeterminate (15)
USP7	6		Total indeterminate (90)
α4β7	1		3700 (150)

As seen in Table 3, the total number of filings related to the key targets is typically on the order of hundreds, if not thousands, and the leading filer has tens to hundreds of filings. The discrepancy in volume of filings can be partially explained by the AI drug discovery companies being founded relatively recently. However, this does not change the reality that there are significantly more patents (and possible competitors owning patents) that could be used against emerging AI drug discovery companies than vice versa. Therefore, there may be opportunities left on the table for these companies to develop valuable patents, including (but not limited to) for defensive and/or cross-licensing purposes.

Conclusions

Here are key takeaways from this analysis:

AI drug discovery companies have patent portfolios that are generally balanced between their AI/ML/computational technologies and their drug compositions and other more traditional pharmaceutical/biotechnology inventions. This can reflect several different scenarios that result in similar portfolio makeup: companies that shifted over time from traditional to AI/ML inventions; companies that begin with a balance across the two; and the patent publication lag obscuring more recent filings that might be more AI/ML focused.

These companies may be significantly undervaluing their ability to explicitly protect technologies directed to key targets, especially with the patent cliff coming later this decade. At least on the AI/ML side, this could be because the AI/ML inventions themselves could be broad enough to apply to any target, or may be directed to applications other than target identification and/or drug discovery.

Given the rapid advancement in this space and changes coming in the overall biotechnology/pharmaceutical patent landscape over the next 5-10 years, AI drug discovery companies have a massive opportunity to create strategic advantages with their patent protection. In particular, they can develop protection for drug development across a wide variety of drug and target classes to complement more conventional, composition-focused patents. This protection can prove valuable for direct enforcement against competitors, as well as strategic partnerships and licensing across the biotech/pharma sector.

AI in Health Care Series

For additional thinking on how artificial intelligence will change the world of health care, click here to read the other articles in our series.

[1] There is generally an 18-month lag between when patent applications are filed and when they are first published. This can make it challenging to draw strong conclusions about the patent portfolios of early-stage companies and/or companies in rapidly evolving technology areas.

[2] To keep things manageable, these analyses are limited to U.S. patents/applications.

[3] Filings were categorized first based on whether the titles clearly mapped to drug composition or conventional technologies vs. AI/ML technologies and second based on keyword, if necessary. Various cases had overlap between these two categories; some were still categorized as relating to AI/ML even if such technologies focused more on data analysis rather than initial drug design.

[4] See Table 1 in the BioPharmaTrend article; Table 2 here does not delineate amongst targets by phase.

[5] Simply listing a target in a patent filing does not mean a patent resulting from that filing will actually protect the target, but being over-inclusive in this manner provides a first step for contrasting the AI company portfolios with those of more established firms.

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Cancer Drugs: Strategies For Patenting Antibody-Drug Conjugate Inventions

bklemm@foley.com — Mon, 29 Apr 2024 15:45:41 +0000

Antibody-drug conjugate (ADC) is a promising class of cancer treatments with accelerating U.S. Food and Drug Administration (FDA) approval and rapidly growing market size as discussed in previous articles in this series. This article discusses patent strategies for ADC inventions.

Introduction

ADCs provide abundant opportunities for new cancer treatments, innovation, and collaboration across different industries because they combine three distinct technologies: (1) an antibody, (2) a toxic payload, and (3) a linker that joins them. While ADCs are based on the seemingly simple idea of using the targeting ability of antibodies to deliver more toxically potent drugs to specific cancer cells, ADCs are complicated molecules that pose significant technical, regulatory, and intellectual property challenges.

Indeed, development of an effective and safe ADC therapy requires significant research and innovation to ensure that the right components are combined in the right way to avoid side effects, inefficiency, tumor resistance, and pharmacokinetic profiles that make the drug delivery unpractical. In some cases, overcoming these challenges requires development of new generations of ADC modalities by using novel payloads, modified antibody backbones, and new drug linker-release mechanisms, as reviewed in .

Even though the development of an effective ADC requires intense research to find the right combination, Patent Offices or Courts may find ADCs to be an “obvious” combination in cases where the components of the ADC were previously used for the same purpose as the ADC. Obviousness may present a major obstacle for patent protection of ADC innovations if the patent claims are not sufficiently supported by evidence and patent prosecution strategies.

Strategies For Addressing Obviousness Challenges To ADC

The Legal Standard for Obviousness

The current framework for analyzing obviousness was established by the Supreme Court decision in KSR International Co. v. Teleflex Inc., 550 U.S. 398, 418 (2007). According to KSR, when a claimed invention is rejected for obviousness because the invention appears to be a combination of known elements, the Examiner must, “identify a reason that would have prompted a person of ordinary skill in the relevant field to combine the elements in the way the claimed new invention does.” See KSR, 550 U.S. at 401. The Federal Circuit further explained in post-KSR decisions that the mere plausibility of prior art combinations is insufficient to establish a prima facie case of obviousness. See PersonalWeb Tech v. Apple, 848 F.3d 987, 994 (Fed. Cir. 2017) stating that:

[The] reasoning seems to say no more than that a skilled artisan, once presented with the two references, would have understood that they could be combined. And that is not enough: it does not imply a motivation to pick out those two references and combine them to arrive at the claimed invention.

A prima facie case of obviousness may be rebutted by submitting objective evidence that the claimed invention provided unexpected results. The post-KSR decision in Bristol-Myers Squibb Co. v. Teva Pharmaceuticals USA, Inc., 752 F.3d 967, 977 (Fed. Cir. 2014) determined that “particularly probative” evidence of unexpected results “establish[es] that there is a difference between the results obtained and those of the closest prior art, and that the difference would not have been expected by one of ordinary skill in the art at the time of the invention.”

Thus, an ADC should not be held obvious merely because the different components of the ADC were previously known. Rather, the ADC can be held obvious if sufficient guidance or motivation to combine the specific components the same way as in the claimed ADC has been established. Even if there is motivation to combine known components into a specifically claimed ADC, the ADC may still be patentable if the ADC provides unexpected results. Therefore, patenting ADCs with known components will often hinge on finding reasons a skilled artisan would not combine the specific ADC components or showing that the particular ADC yielded unexpected results compared to the components, as further discussed and illustrated below.

Demonstrating a lack of motivation to make the claimed combination

1) ADC therapy claims may be nonobvious because they recite features that the antibody portion of the ADC failed to perform by itself.

Patent claims covering a particular ADC therapy may be found nonobvious due to a lack of motivation to combine known elements into the claimed ADC if the antibody and/or conjugate failed to treat a particular patient population individually. For example, U.S. Patent 7,575,748 survived an Inter Partes Review (IPR) challenge based on obviousness because the claimed ADC could be used to treat a particular indication that the antibody portion of the ADC could not treat by itself as highlighted in the quoted claim language below. See Phigenix, Inc. v. Genentech, Inc. and Immunogen, Inc., IPR2014-00842. The allowed claims in U.S. 7,575,748 recited:

A method for the treatment of a tumor in a mammal, comprising the steps of

(i) identifying said tumor as being characterized by overexpression of an ErbB2 receptor and as being a tumor that does not respond, or responds poorly, to treatment with an anti-ErbB antibody, and

(ii) intravenously administering to the mammal a therapeutically effective amount of a conjugate of a humanized antibody huMab 4D5-8 covalently linked via a thioether linking group with a maytansinoid DM1….

U.S. Patent 7,575,748 illustrated that method claims covering an ADC formed by known components can be found allowable based on properties of the ADC.

2) Establishing that a component of the ADC was discouraged

U.S. Patent 8,337,856 is directed to ADC composition claims, and upon challenge the claims were held valid even though they recited known antibodies and conjugates. See Phigenix, Inc. v. Immunogen, Inc., Case IPR-2014-00676, Final Written Decisions dated October 27, 2015 (Paper 39). Independent claim 1 of the ‘856 patent recites:

1. An immunoconjugate comprising an anti-ErbB2 antibody conjugated to a maytansinoid, wherein the antibody is huMAb4D5-8.

huMAB4D5-8 was commercialized in the prior art product, Herceptin®, and used for treating breast cancer in combination with other cytotoxic agents. In addition, maytansinoid had already been used as a conjugate to different antibodies. The patent challenger, therefore, argued that it would be obvious to use Herceptin® with maytansinoid.

However, the Patent Owner successfully presented evidence suggesting to a skilled artisan that “Herceptin-maytansinoid immunoconjugates would have been expected to exhibit unacceptable levels of antigen-dependent toxicity in normal human liver tissue in patients.” See pages 16–22 of IPR-2014-00676 (Paper 39). The Board found this argument persuasive because the patent challenger had not explained that an ordinary artisan would have been motivated to make the claimed ADC, given the reported liver toxicities of maytansinoid immunoconjugates.

Given these cases, strategies for overcoming obviousness rejections could be found by determining if:

There are known problems of toxicity associated with the toxic payload component of the ADC or other reasons not to use the toxic payload as claimed, and
The antibody portion of the ADC has previously been reported to be ineffective by itself against the claimed indication.

As will be discussed further below, the claims of U.S. Patent 8,337,856 were also found valid because of unexpected results, which is another central strategy for obtaining patent coverage of ADCs.

Unexpected Results achieved by the ADC

1) Example of ADC claims found valid based on unexpected results

The claims of U.S. Patent 8,337,856 were found valid based on unexpected results even though the components of the claimed ADC were known. See Immunogen, Inc., IPR-2014-00676 (Paper 39). In particular, the Board held that the Herceptin-maytansinoid immunoconjugate claims were non-obvious because the Patent Owner provided substantial evidence of unexpectedly superior results compared to the “closest prior art” composition. See pages 23-25, Immunogen, Inc., IPR-2014-00676 (Paper 39). The “closest prior art” was the antibody by itself, and the results demonstrated that the ADC overcame some limitations of the “naked” antibody.

2) Example of evidence of unexpected results not sufficient to establish nonobviousness

Demonstrating unexpected results can be challenging, as shown in Hospira v. Genentech, IPR2017-00731 (Paper 120, at page 23 (PTAB October 3, 2018)), where the claims of U.S. Patent No. 7,846,441 were found unpatentable. The claims of U.S. Patent No. 7,846,441 are represented by claim 1:

1. A method for the treatment of a human patient with a malignant progressing tumor or cancer characterized by overexpression of ErbB2 receptor, comprising administering a combination of an intact antibody which binds to epitope 4D5 within the ErbB2 extracellular domain sequence and a taxoid, in the absence of an anthracycline derivative, to the human patient in an amount effective to extend the time to disease progression in said human patient, without increase in overall severe adverse events.

The ADC covered by this patent contains an antibody that binds to epitope 4D5 within the ErbB2 extracellular domain sequence and a taxoid. A previous publication asserted against this patent disclosed the same antibody conjugated with a taxoid tested in a mouse model. The Patent Owner argued that the claimed method yielded unexpected results in humans as compared to the previous mouse studies. However, the Board held that a mouse study is a “reliable predictor of success in humans,” and the results from the mouse study in prior art predict that the ADC would also be effective in humans. See Hospira v. Genentech, IPR2017-00731 (Paper 120, at page 26 (P.T.A.B. Oct. 3, 2018).

In addition, the Board in Hospira v. Genentech found the Patent Owner’s statements to the FDA to be evidence of obviousness of the claims. The Patent Owner had requested FDA approval of a combination of an antibody binding ErbB2 (trastuzumab) and a taxoid (paclitaxel) because a skilled artisan would expect that this combination was effective based on the same prior art as asserted against U.S. Patent 7,846,441. Hence, the Board considered the Patent Owner’s statements at the FDA as evidence that the results obtained with the claimed ADC were expected. See Hospira v. Genentech, IPR2017-00731 (Paper 120, at pages 27-28 (P.T.A.B. Oct. 3, 2018).

The argument for nonobviousness based on unexpected results in Hospira v. Genentech failed because the closest prior art was a combination of the same components as the claimed ADC. Accordingly, in cases where the closest prior art to a claimed ADC only discloses one of the components of the ADC, the availability of beneficial results obtained with the ADC will help obtaining allowance of the claims or surviving a challenge based on obviousness.

Main Takeaways

Based on the above review, we provide the following considerations for determining strategies for obtaining patent coverage of ADCs in cases where the components of the ADC are known:

1)    Is use of one of the ADC components discouraged for the claimed method (e.g., would it be too harmful)?

2)    Could the selected payload interfere with the properties of the antibody or vice versa (e.g., cause aggregation, lower solubility, or changes to important post-translational modifications of the antibody)?

3)    Have the components of the ADC been used for treating the same indications or is there a lack of guidance for using one or more of the ADC components as claimed (i.e., lack of motivation)?

4)    Is objective evidence of unexpected results from the ADC available (such as improved efficacy or tolerability as compared to individual components)?

Finally, it should be noted that the linker chemistry is also an important part of the ADC and patentability of the ADC. The ADC linker was not central to the above discussed cases because it was not recited by the independent claims. However, the Board did analyze dependent claims reciting specific non-cleavable linkers IPR-2014-00676, which would further distinguish U.S. Patent 8,337,856 from prior art. Accordingly, linker chemistry may confer patentability to an ADC.

Indeed, the linker chemistry offers fertile grounds for innovation and development of new generations of ADCs. See Tsuchikama, K. et al., Nature Rev. Clin. Oncol., 21, 203–223; 2024. The linker chemistry can, for example, be used to regulate the amount of toxic payload (i.e., the drug-antibody ratio), and when and where the payload is released to improve the efficacy and tolerability of the ADC. Even though the linker chemistry by itself may not be new, there may be a lack of motivation to use a particular linker strategy in an ADC because the linker interferes with the antibody structure, changes post-translational modification of the antibody, causes aggregation, or provides too high or too low payload-antibody ratio. Thus, a careful consideration of the linker and its effects on the function of the ADC could also be critical for obtaining patent protection of the ADC.

Ultimately, successful strategies of patenting ADCs will depend on the results obtained with the ADC, and what results would be expected based on common knowledge of the antibodies, toxic payloads, and linker chemistry that make up the ADC.

For additional resources on Antibody Drug Conjugates (ADCs) and how they will change the health care & life sciences and technology industries, click here to read the other articles in our series.

�鶹ֱ�� is here to help you address the short- and long-term impacts in the wake of regulatory changes. We have the resources to help you navigate these and other important legal considerations related to business operations and industry-specific issues. Please reach out to the authors, your �鶹ֱ�� relationship partner, our Health Care & Life Sciences Sector, or to our Innovative Technology Sector with any questions.

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Cancer Drugs: Antibody Drug Conjugates (ADCs) Keep Growing

bklemm@foley.com — Tue, 12 Mar 2024 15:27:06 +0000

Over the past few years economic headwinds have resulted in fewer deals, with companies and private equity firms alike reassessing where to spend money. But one portion of the pharmaceutical industry is bucking this trend in a major way. Since 2018, licensing deals totaling over US$60 billion have been signed regarding ADCs, with 2023 alone having at least 18 deals. As a result, worldwide ADC sales are projected to reach US $20-30 billion per year in the near future.

ADCs are a class of chemotherapy medicines used in the treatment of cancers. These drugs combine an antibody and cancer treating drug to minimize the impact of chemotherapy on the healthy tissue in patients, while maximizing their impact on the cancer. The antibody is chosen to target the specific cancers cells and guide the cancer drug to the specific part of the body requiring treatment. Earlier cancer treatments were unable to localize the impact of the powerful chemotherapy drugs and caused more collateral damage in the body. ADCs do a better job of specifically targeting the part of the body being treated. This can also lead to a smaller dose of the cancer drug in the body, further reducing the overall negative impact on the patient while still treating the cancer.

ADCs are not a new technology, as the first ADC was approved in 2000 to treat leukemia. Continued development of the technology has given regulators, pharmaceutical companies, and doctors more confidence in the clinical efficacy of these treatments. More than 140 new ADCs are currently in clinical development.

The rapid expansion of this area of technology raises many business and legal questions. We will explore many of these throughout the upcoming articles in this series. These topics include:

Issues related to deals and licensing for ADCs/antibody therapeutics
Patent/IP challenges to consider for ADCs/antibody therapeutics
Clinical trial issues for ADCs/antibody therapeutics
FDA/regulatory issues for ADCs/Antibody therapeutics
Litigation issues for ADCs/antibody therapeutics

�鶹ֱ�� is here to help you address the short- and long-term impacts in the wake of regulatory changes. We have the resources to help you navigate these and other important legal considerations related to business operations and industry-specific issues. Please reach out to the authors, your �鶹ֱ�� relationship partner, our Health Care & Life Sciences Sector, or to our Innovative Technology Sector with any questions.

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Leveraging Risk Management Frameworks for AI Solutions in Health Care

bklemm@foley.com — Tue, 27 Feb 2024 15:35:58 +0000

As recently described by , the liability risks associated with using artificial intelligence (AI) in a health care setting are substantial and have caused consternation among sector participants. To illustrate that point:

“Some attorneys counsel health care organizations with dire warnings about liability and dauntingly long lists of legal concerns. Unfortunately, liability concern can lead to overly conservative decisions, including reluctance to try new things.”

“… in most states, plaintiffs alleging that complex products were defectively designed must show that there is a reasonable alternative design that would be safer, but it is difficult to apply that concept to AI. … Plaintiffs can suggest better training data or validation processes but may struggle to prove that these would have changed the patterns enough to eliminate the “defect.”

Accordingly, the article’s key recommendations include (1) a diligence recommendation to assess each AI tool individually and (2) a negotiation recommendation for buyers to use their current power advantage to negotiate for tools with lower (or easier to manage) risks.

Creating Risk Frameworks

Expanding from such considerations, we would guide health care providers to implement a comprehensive framework that maps each type of AI tool to specific risks to determine how to manage these risks. Key factors that such frameworks could include are outlined in the table below:

Factor	Details	Risks/Principles Addressed
Training Data Transparency	How easy is it to identify the demographic characteristics of the data distribution used to train the model, and can the user filter the data to more closely match the subject that the tool is being used for?	Bias, Explainability, Distinguishing Defects from User Error
Output Transparency	Does the tool explain (a) the data that supports its recommendations, (b) its confidence in a given recommendation, and (c) other outputs that were not selected?	Bias, Explainability, Distinguishing Defects from User Error
Data Governance	Are important data governance processes built into the tool and agreement to protect both the personal identifiable information (PII) used to train the model and used at runtime to generate predictions/recommendations?	Privacy, Confidentiality, Freedom to Operate
Data Usage	Have appropriate consents been received (1) by the provider for inputting patient data to the tool at runtime and (2) by the software developer for the use of any underlying patient data for model training?	Privacy/Consent, Confidentiality
Notice Provisions	Is appropriate notice given to users/consumers/patients that AI tools are being used (and for what purpose)?	Privacy/Consent, Notice Requirement Compliance
User(s) in the Loop	Is the end user (i.e., clinician) the only person evaluating the outputs of the model on a case-by-case basis with limited visibility as to how the model is performing under other conditions, or is there a more systematic way of surfacing outputs to a risk manager who can have a global view of how the model is performing?	Bias, Distinguishing Defects from User Error
Indemnity Negotiation	Are indemnities appropriate for the health care context in which the tool is being used, rather than a conventional software context?	Liability Allocation
Insurance Policies	Does current insurance coverage only address software-type concerns or malpractice-type concerns vs. bridging the gap between the two?	Liability Allocation, Increasing Certainty of Costs Relative to Benefits of Tools

As both AI tools and the litigation landscape mature, it will become easier to build a robust risk management process. In the meantime, thinking through these kinds of considerations can help both developers and buyers of AI tools manage novel risks while achieving the benefits of these tools in improving patient care.

AI in Health Care Series

For additional thinking on how artificial intelligence will change the world of health care, click here to read the other articles in our series.

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NIST Publishes Final “Cybersecurity Resource Guide” on Implementing the HIPAA Security Rule

bklemm@foley.com — Wed, 21 Feb 2024 15:05:41 +0000

In an important development for HIPAA-regulated entities looking for practical assistance in understanding, implementing, and enhancing compliance with the , the National Institute of Standards and Technology (NIST) has finalized its comprehensive guidance, (Resource Guide). This release follows the initial draft that NIST published for public comment in July 2022 and builds on NIST’s foundational 2008 publication. The updated Resource Guide comes at the heels of the U.S. Department of Health and Human Services (HHS) releasing across the health sector last month and a for the health care sector in December of 2023.

As a technology-neutral framework, the HIPAA Security Rule recognizes the diversity in the size, complexity, and capabilities of regulated entities, offering a flexible and scalable approach to safeguarding electronic protected health information (ePHI). Acknowledging that no single compliance strategy fits all organizations, the Resource Guide presents an extensive set of guidelines that entities may adapt in part or in full to strengthen their cybersecurity posture and achieve compliance with the HIPAA Security Rule. Moreover, the Resource Guide is structured to cater to various organizational needs and maturity levels in cybersecurity practices. It emphasizes that risk assessment and risk management processes are crucial to a regulated entity’s compliance with the HIPAA Security Rule and the protection of ePHI.

Below is an overview of the content covered by the Resource Guide:

Considerations When Applying the HIPAA Security Rule

Perhaps most helpful is that NIST has broken each HIPAA Security Rule standard down by key activities that a regulated entity may wish to consider implementing, adding a detailed description, and providing sample questions to guide entities in their compliance efforts. This detailed guidance for each HIPAA Security Rule standard will be helpful for regulated entities struggling to adopt it with only the language in the HIPAA Security Rule and HHS guidance on the same.

In an accessible, tabular format, the Resource Guide outlines considerations for implementing the HIPAA Security Rule, highlighting:

Key Activities: Actions typically associated with the security functions suggested by each standard.
Description: Expanded explanations of these activities, detailing strategies for implementation.
Sample Questions: Thought-provoking questions for self-assessment, aimed at gauging whether the standard has been adequately implemented. Negative responses to these questions should prompt further action to ensure compliance.

As an illustrative example, consider the standard on , which mandates the implementation of policies and procedures to address security incidents. A key activity highlighted is “Developing and deploying an incident response team or other reasonable and appropriate response mechanism.” To assist entities in evaluating their readiness and implementation of this standard, NIST provides sample questions such as:

Do members of the team have adequate knowledge of the organization’s hardware and software?
Do members of the team have the authority to speak for the organization to the media, law enforcement, and clients or business partners?
Has the incident response team received appropriate training in incident response activities?

To further aid organizations seeking to implement the HIPAA Security Rule, NIST also updated its Cybersecurity and Privacy Reference Tool (). The CPRT displays HIPAA Security Rule regulations, complemented with direct links to further NIST tools and resources for enhanced understanding and implementation.

Risk Assessment Guidelines

The Risk Assessment Guidelines section of the Resource Guide provides a methodology for conducting a risk assessment. The requires that all regulated entities “[c]onduct an accurate and thorough assessment of the potential risks and vulnerabilities to the confidentiality, integrity, and availability of electronic protected health information held by the covered entity or business associate” and then “[i]mplement security measures sufficient to reduce risks and vulnerabilities to a reasonable and appropriate level.” This is known as the security risk analysis and risk management plan, respectively. The results of the security risk analysis should enable regulated entities to identify appropriate security controls for reducing risk to ePHI. NIST’s guidance with respect to risk assessments is similar to previous HHS guidance provided at the and :

Prepare for the Assessment. Understand where ePHI is created, received, maintained, processed, or transmitted. This must include all parties and systems to which ePHI is transmitted, including remote workers, external service providers, and medical devices that process ePHI.
Identify Realistic Threats. Identify potential threat events and sources, including (but not limited to) ransomware, insider threats, phishing, environmental threats (e.g., power failure), and natural threats (e.g., flood).
Identify Potential Vulnerabilities and Predisposing Conditions. Identify vulnerabilities or conditions that can be exploited for the threats identified in Step 2 to have an impact.
Determine the Likelihood of a Threat Exploiting a Vulnerability. For each threat identified in Step 2, determine the likelihood of a threat exploiting a vulnerability. A low, moderate, or high-risk scale is commonly used but not required.
Determine the Impact of a Threat Exploiting a Vulnerability. The regulated entity should select an impact rating for each identified threat/vulnerability pair and may consider how the threat event can affect the loss or degradation of the confidentiality, integrity, and/or availability of ePHI. Example impacts would include an inability to perform business functions, financial losses, and reputational harm. Again, a low, moderate, or high-risk scale is commonly used but not required.
Determine the Level of Risk. The level of risk is determined by analyzing the overall likelihood of threat occurrence (Step 4) and the resulting impact (Step 5). A risk-level matrix can be helpful in determining risk levels for each threat event/vulnerability pair.
Document the Results.

Similar to previous HHS guidance, NIST reminds regulated entities that the risk assessment is an ongoing activity, not a one-off exercise. The assessment must be “updated on a periodic basis in order for risks to be properly identified, documented, and subsequently managed.” The cybersecurity landscape is ever-evolving, with threats morphing and new vulnerabilities emerging even as existing ones are mitigated. Additionally, changes in an organization’s operations, such as the introduction of new policies or technologies, can alter the likelihood and impact of potential threat events. This dynamic context underscores the necessity for risk assessments to be periodically revisited and updated. Such regular updates ensure that risks are accurately identified, documented, and managed in a timely and effective manner, aligning with the organization’s evolving risk profile and enhancing its cybersecurity posture.

Moreover, failure to have a thorough and up-to-date risk assessment is one of the top failures documented by HHS in with regulated entities. Therefore, regulated entities should take this opportunity to determine when its last risk assessment was conducted, ensure the risk assessment meets previous HHS guidance, and consider the NIST guidance in this Resource Guide as well.

Risk Management Guidelines

NIST states that the Risk Management Guidelines introduce a “structured, flexible, extensible, and repeatable process” that regulated entities may utilize for managing identified risks and achieving risk-based protection of ePHI. The regulated entity will need to determine what risk rating poses an unacceptable level of risk to ePHI, given the regulated entity’s risk tolerance and appetite. Ultimately, the regulated entity’s risk assessment processes should inform its decisions regarding the implementation of security measures sufficient to reduce risks to ePHI to levels within organizational risk tolerance.

To illustrate, consider a scenario where an organization identifies a high risk to ePHI from ransomware attacks, characterized by both a high likelihood and a high impact. Upon implementing critical security measures—namely, , , and —the organization reassess and significantly lowers the risk level from “High” to “Low.” Although the likelihood of such an attack remains high, the impact is now considered low due to these proactive measures, aligning the risk with the organization’s risk tolerance.

Conclusion

NIST’s Resource Guide should serve as an essential resource for HIPAA-regulated entities, offering guidance on risk assessment, management, and compliance with the HIPAA Security Rule. In leveraging the Resource Guide, organizations can maintain robust protection for ePHI and adapt to changes in the cybersecurity landscape.

In addition to the Resource Guide itself, NIST has also provided on its website to further assist HIPAA-covered entities and business associates with strategies to improve their cybersecurity in specific areas including Telehealth/Telemedicine, Mobile Device Security, Medical Device Security, Cloud Services, Incident Handling/Response, and others.

For more information or assistance regarding compliance with the HIPAA Security Rule, please contact either of the authors of this article or any other Partner or Senior Counsel member of �鶹ֱ��’s Technology Transactions, Cybersecurity, and Privacy Group or Health Care Practice Group.

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FDA Approves Two Gene Therapies to Treat Sickle Cell Disease Including a CRISPR-Based Therapy

bklemm@foley.com — Fri, 08 Dec 2023 19:45:30 +0000

On December 8, 2023 the U.S. Food and Drug Administration (FDA) , both cell-based gene therapies for the treatment of sickle cell disease in patients 12 and older. Further, Casgevy is the first approved treatment utilizing CRISPR, an innovative gene editing technology that was the basis of the research leading to the 2020 Nobel Prize in Chemistry.

Sickle Cell Disease

Sickle Cell Disease affects about 100,000 patients in the U.S., predominantly in the African-American community. A mutation in hemoglobin, the protein which carries oxygen in red blood cells, leads to a change in the red blood cell shape so the red blood cells resemble a sickle. Unfortunately, this sickle shape restricts the flow of red blood cells, limits oxygen delivery to tissues, causes pain, and results in organ damage called vaso-oclusive events (VOEs) or vaso-oclusive crises (VOCs).

Casgevy

Casgevy is approved for the treatment of sickle cell disease in patients with recurrent VOCs, attaining status as the first approved treatment leveraging CRISPR/Cas9 to edit the patients’ cells. During this process blood stem cells are removed from the patient, modified with CRISPR/Cas9, and returned to the patient using a one-time, single dose infusion. Prior to treatment, the patient’s blood stem cells are collected and the patient undergoes chemotherapy to remove stem cells from the bone marrow. The treatment then replaces the stem cells in the bone marrow with modified stem cells, allowing the patient to produce blood cells without the sickle trait.

During clinical studies, 44 patients were treated and 31 patients had enough time to be evaluated. 29 (93.5%) achieved the primary outcome of freedom from further VOC episodes.

Lyfgenia

Lyfgenia also modifies the patients’ cells but uses a lentiviral vector instead of the CRISPR technology. The process for treating the patient is similar to Casgey, as cells are removed from the patient and modified before being re-infused to the bone marrow. The modified blood stem cells produce HbA^T87Q, a gene-therapy derived hemoglobin which acts similar to hemoglobin A, the normal adult hemoglobin produced by cells not impacted by sickle cell disease.

Casgevy and Lyfgenia both received , , and designations.

Implications

Gene and cell-based therapies are an exciting area of growth in the pharmaceutical space. With these approvals, new treatments will be available for patients suffering from sickle cell disease. Of particular note is that a CRISPR therapy is now available; the CRISPR/Cas9 gene editing technology is ubiquitous in the research and development world and the technology is now making an important step into the clinical realm as well.

Further, it’s exciting that CRISPR, a gene editing technique well suited to make single base pair changes in DNA, is now being harnessed to help patients suffering from diseases with point mutations. This approval opens the possibility of similar treatments in other diseases for patients desperately seeking assistance.

For more information, to read the full FDA press release.

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Safeguarding AI Innovation in Stem Cell Therapy

bklemm@foley.com — Mon, 23 Oct 2023 16:58:59 +0000

The use of artificial intelligence (AI) to advance stem cell therapy has produced exciting results, with a key role in driving recent growth and innovation. Separated into three parts, this article provides an overview the promises and challenges of stem cell therapy before exploring the current uses of AI to address these challenges. In the third part and final section, it then outlines strategic considerations for addressing key patent eligibility issues likely to arise when patenting AI-enabled stem cell inventions. Identifying inventions involving a combination of AI and stem cell technology and ensuring appropriate patent protection of these inventions is crucial for safeguarding the advances.

Promises and Challenges of Stem Cell Therapy

The stem cell field has made significant advancements, such as discovering how to induce any cell into a stem cell and how to use stem cells for augmenting muscle regeneration, rebuilding heart tissue, developing brain models (organoids) for studying mental disorders, producing islets for treating diabetes, and many other promising developments as discussed in a recent . Moreover, TechSci Research reported that the market for stem cell therapy is expected to grow an impressive 12.39% annually through 2028.

While the stem cell field is rapidly growing, it also faces significant hurdles that need to be overcome. Stem cell therapies require rigorous testing and regulatory approval because stem cells can behave unpredictably during manufacturing and differentiation to functional cell types as discussed in detail in a recent article.

That being said, AI is rapidly becoming central for innovative solutions to remove these obstacles facing stem cell therapy.

Using AI to Aid Stem Cell Therapy

Private companies, academic organizations, and government institutions now incorporate AI to find solutions for using stem cells to treat diseases. We discuss below four major areas where AI has already produced promising results.

AI-Enabled Models Predicting Stem Cell Behavior

The is building a “virtual cell” to simulate the key features and behavior of any cell type in the human body. The hope is that scientists can use this “virtual cell” to predict how different cells, including stem cells, will respond to external stimuli. This initiative will use the vast knowledge consolidated by global consortiums such as the Whole-Organism Cell Atlas to build a reference map of every cell type in the body. Training AI on such extensive knowledge may provide a system that can accurately predict how cells are generated from stem cells, how they will interact in the human body, how diseases cause loss of cells or cellular functions, and how to use stem cells for curing such diseases.

and co-workers at the National Institute of Standards and Technology and the National Eye Institute reported a powerful illustration of how AI can be used to predict cellular behavior and improve stem cell differentiation. This study demonstrated that an AI system can predict the differentiation of stem cells towards eye cells, which was used to significantly improve the quality of this process.

New Tools for Identifying Cells or Cellular States

AI is also providing new tools for identifying cells or cellular states. and co-workers developed an AI-enabled microscopy imaging system to identify stem cells that initiate differentiation without relying on molecular markers and demonstrated that “lineage choice can be detected up to three generations before conventional molecular markers are observable.” and co-workers demonstrated that such AI and microscopy systems can significantly reduce the variability of stem cell differentiation.

Simultaneously, and co-workers developed an AI system called DeepACT that can identify healthy and productive skin stem cells with the same accuracy as humans. This system allows the selection and enrichment of clinically valuable cells.

AI-enabled identification of cells and prediction of their behavior in complex culture systems will help make stem cell differentiation more predictable and improve the production of clinically relevant cells with greater accuracy and efficiency.

Automated Manufacturing of Stem Cells

AI can also provide sophisticated systems for large-scale manufacturing of pure stem cell populations, which is essential for successful stem cell therapy. For example, (founder of Cellino Biotech) developed a system combining stem cell biology, machine learning, and laser physics to automate stem cell manufacturing. Laser editing allowed both removing unwanted cells and delivering cargo to individual cells. The automated AI-enabled manufacturing of high-quality cell batches may be critical for providing the large numbers of patient-specific cells with low batch-to-batch variability needed for clinical trials and commercialization of stem cell therapy.

Advancing Organ-on-a-Chip Technology

Researchers have previously used stem cells to produce organoids-on-a-chip that offer better clinical predictability of drug properties than conventional cell culture systems, as a study by AstraZeneca demonstrated. (sometimes called patients-on-a-chip) are three-dimensional mini-organs that have structural and functional characteristics of native organs. Moreover, developed a model system containing several interconnected organs (called human-on-a-chip), allowing an even more sophisticated evaluation of drugs on a system of organs.

While the organoids-on-a-chip technology provides a powerful tool for drug screening and validation, this technology is difficult to scale because of variability from organoid to organoid, and it is labor-intensive to make them work on a large scale due to the complexity of these systems. and co-workers reviewed multiple ways of addressing this issue using AI systems. In particular, AI can aid with bio-printing of organoids to facilitate the automation of manufacturing organoids-on-a-chip with reduced variability, as reported by .

Accordingly, combining organ-on-a-chip technology with AI will significantly boost stem cell-based drug discovery and validation by providing improved methods for producing high-quality organoids.

Strategies for Patent Protection of AI-Enabled Stem Cell Technology and Satisfying the Patent Eligibility Requirement Under 35 USC § 101

The increased use of AI in stem cell research and clinical development of stem cell therapies will generate many opportunities for innovation and growth, as shown above. Intellectual property rights will be particularly important for driving innovation in this area, as advances will require the collaboration of various companies, academic organizations, and government institutions, which all have different expertise and available resources. Collaboration among all these different actors often results in intellectual property concerns, in particular patent owner rights to inventions existing before the collaboration as well as inventions arising from the collaboration.

Stakeholders should carefully monitor inventions from combining AI and stem cells and look for patent-eligible concepts at every stage to avoid issues that would frustrate commercial interests. In this regard, it is important to consider 35 USC § 101 of the Patent Act because it determines what categories of inventions are patent eligible if the other requirements are met and can be a crucial hurdle to overcome for many types of inventions.

Patent Eligibility Requirement Under 35 USC § 101

Section 101 of the Patent Act states that processes, machines, manufactures, and compositions of matter, as well as improvements thereof, are eligible for patent protection. Interpreting this statute, the Supreme Court ruled in Diamond v. Chakrabarty, 447 U.S. 303 (1980) that man-made living organisms are eligible for patent protection, but also affirmed that no patents should be granted on laws of nature, natural phenomena, and abstract ideas. Later, the Supreme Court decisions in Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 US 208 (2014) and Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 US 66 (2012) set forth a two-part test for determining patent eligibility that can pose a significant obstacle to patenting certain software and computer-implemented systems like AI. The Mayo/Alice test first determines if the claims are directed to a judicial exception, such as an abstract idea, a law of nature, or a natural phenomenon. If the claims are directed to a judicial exception, then the second step determines whether the claim recites additional elements that amount to significantly more than the judicial exception.

While living organisms and biological molecules may be patent eligible if they are man-made, the Supreme Court in Ass’n for Molecular Pathology v. Myriad Genetics, Inc., 569 US 576 (2013) determined that isolated DNA is a naturally occurring product and patent-ineligible subject matter under 35 USC § 101. Moreover, In re Roslin Institute (Edinburgh), 750 F.3d 1333 (Fed. Cir. 2014) it was concluded that the claims were directed to a cloned farm animal such as a sheep, and therefore not “markedly different” from a naturally occurring animal. Patent eligibility of living organisms and biological molecules will therefore require showing that the invention is markedly different from its naturally occurring counterpart.

Strategies for Overcoming the Patent Eligibility Requirement Under 35 USC § 101

While satisfying the patent eligibility requirement is determined on a case-by-case basis and will be very fact-dependent, the court decisions addressing these issues provide some general strategies for overcoming patent eligibility issues. Below are three approaches that may be particularly relevant when seeking to protect AI-enabled stem cell technology.

a) Does the claimed invention improve or solve a problem facing stem cell therapy?

Even if the Mayo/Alice test has created difficulties in obtaining software patents on some computer-implemented technology, an invention deemed directed to patent-ineligible subject matter has nevertheless been found patent-eligible if the claimed invention provides technological improvement. See, e.g., Enfish, L.L.C. v. Microsoft Corp., 822 F.3d 1327, 1336-37, (Fed. Cir. 2016) where the patentee successfully argued that its claimed self-referential table for a computer database was an improvement to an existing technology and thus not directed to an abstract idea. In CardioNet, L.L.C. v. InfoBionic, Inc., 955 F.3d 1358 (Fed. Cir. 2020), the Federal Circuit found the claimed cardiac monitoring system patent eligible because it was not merely an abstract idea of distinguishing between atrial fibrillation and flutter caused by irregular heartbeats, but achieved improvement over conventional monitoring devices. Demonstrating technological improvement was also successfully used to satisfy the patent eligibility requirement in, for example, Visual Memory LLC v. NVIDIA Corp., 867 F.3d 1253 (Fed. Cir. 2017), Amdocs (Israel) Ltd. v. Openet Telecom, Inc., 841 F.3d 1288 (Fed. Cir. 2016), and Rapid Litig. Mgmt. v. CellzDirect, Inc., 827 F.3d 1042 (Fed. Cir. 2016).

Therefore, AI systems that significantly improve stem cell technology could overcome patent eligibility issues by arguing for technological improvement over conventional methods or devices. Practitioners drafting applications to cover AI inventions in the stem cell field that may raise a patent eligibility issue should describe how the claimed inventions improved or solved a problem facing stem cell therapy to provide evidence of technological improvement.

b) Do the claims covering a stem cell recite features markedly different from naturally occurring counterpart cells, and can AI help identify such features?

The patent eligibility requirement under 35 USC § 101 also causes problems for claims directed to stem cells if the claimed cells are not shown to be different from the in vivo counterpart cells. See Ass’n for Molecular Pathology v. Myriad Genetics, Inc. and In re Roslin Institute (Edinburgh).

Patenting stem cells therefore relies on whether it can be shown that the claimed cells are markedly different from naturally occurring counterparts. For example, in Ex Parte Ho, Appeal No. 2016-007472 (PTAB, 2018), the patentee successfully argued that the specific stem cell culture conditions resulted in structural differences between in vivo and in vitro mesenchymal stem cells (MSCs) , and the claims recited MSCs with higher expression levels of specific markers compared to the naturally occurring counterpart cells. While Ex Parte Ho is an appeal decision at PTAB and therefore not binding case law, this decision highlights the importance of identifying features or behaviors of stem cells in culture that could distinguish them from their naturally occurring counterparts.

The rapid advances in using AI to identify and characterize stem cells may result in the discovery of features of stem cells in culture that are not present in vivo, and may provide new options for satisfying the patent eligibility requirement for stem cells.

c) Identifying combinations of steps that result in non-conventional methods even though the individual steps are well-known.

The Federal Circuit Decision Rapid Litig. Mgmt. v. CellzDirect, Inc., 827 F.3d 1042 (Fed. Cir. 2016) found a claimed combination of freezing and thawing steps patent eligible. While the individual steps of the claims were well-known, the court found that the combination of steps was “far from routine and conventional” and provided technological improvement of cell viability.

In view of the CellzDirect, Inc. decision, it is important to consider that integrating AI systems and stem cell technology may result in patent-eligible processes even though the individual steps of the newly developed methods are well-known.

Conclusion

AI has already produced promising results for the development of stem cell technology, as discussed above. Still, the full-scale deployment of these therapies will require further technological development, which can be boosted by the availability of high-quality data for training AI systems and the use of these AI-enabled technologies to produce biological insights into the properties and behavior of stem cells. Further, identification of patent eligible subject matter arising from the convergence of AI and stem cell technology is vital for safeguarding progress and facilitating collaboration among the various actors involved.

AI in Health Care Series

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Health Tech and Genomics Archives | �鶹ֱ�� & Lardner LLP

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Explainability and Replay

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