Keeping pace with the ever-changing life sciences industry, Morrison Foerster stays on top of the trends and political, legal, and economic drivers to support striking the balance between innovative discovery and purposeful business planning to assure our clients continued success. MoFo Life Sciences shares timely legal and business insights and in-depth analyses on trends and the complex technologies at the heart of the global pharmaceutical, biotechnology, diagnostics, medical devices, digital health, and AgTech fields.Learn more about our <a href="https://www.mofo.com/capabilities/life-sciences-healthcare" target="_self">Life Sciences practice</a>.<h2 dir="ltr">ABOUT MORRISON FOERSTER</h2>Morrison Foerster is a leading global law firm that transforms complexity into advantage for its clients. Our clients include some of the largest financial institutions, banks, consulting and accounting firms, and Fortune 100, technology, and life sciences companies. Highlighting the firm’s commitment to client service, leadership in market-changing deals and impact litigation, and values-based culture, Morrison Foerster has been repeatedly recognized as one of the top 10 firms on The American Lawyer’s A-List. Year after year, the firm receives significant recognition from Chambers and The Legal 500 across their various guides, including Global, USA, Asia‑Pacific, Europe, UK, Latin America, and FinTech Legal. Our lawyers passionately care about delivering legal excellence while living our values. Morrison Foerster has a long-standing commitment to creating a culture that respects and celebrates differences, while providing an inclusive environment. The firm has achieved Mansfield Certification Plus since 2018 as a result of successfully reaching at least 30 percent women, communities of color, and LGBTQ+ lawyer representation in a notable number of current leadership roles and committees. The firm also has a long history of commitment to the community and society through providing pro bono legal services, including litigating for civil rights and civil liberties, improving public education and fostering the wellbeing of children, advocating for veterans, promoting international human rights, enforcing the right to asylum, and safeguarding the environment.

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Dr. Meghan Poon counsels clients on patent preparation and prosecution, portfolio strategy, and IP due diligence in the areas of biotechnology and biologics. She has significant experience in diverse technologies including antibodies, cell therapies, gene therapies, diagnostics, and gene-edited plants. She represents clients ranging from large international pharmaceutical companies to midsized companies, startups, and universities.Meghan has filed numerous Patent Term Extension applications to secure additional term for her clients’ patents, which cover FDA-approved products. In addition, Meghan has extensive experience addressing complex obviousness-double patenting and subject matter eligibility issues impacting her clients’ patent portfolios. Meghan also represents clients in both investor-side and target-side due diligence, in deals from seed funding to initial public offerings.Prior to law school, Meghan attended graduate school at the University of California, Los Angeles, where she received a Ph.D. in biological chemistry based on her work on protein-protein and protein-DNA interactions at a molecular level. Meghan is the author of several scientific publications and has presented her research at international conferences.Meghan received her J.D. from the University of Southern California Law School and spent a year as a visiting student at Santa Clara University, where she focused on intellectual property.

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Dr. Meghan Poon counsels clients on patent preparation and prosecution, portfolio strategy, and IP due diligence in the areas of biotechnology and biologics. She has significant experience in diverse technologies including antibodies, cell therapies, gene therapies, diagnostics, and gene-edited plants.

Meghan Poon

Wolfgang advises clients on contentious and non-contentious matters involving their most valuable intellectual property assets in life sciences, digital, and high-tech industries.His subjects of focus include research and development collaborations, complex licensing agreements, and other technology transactions, as well as know-how protection strategies, and employee invention law. He also has substantial experience in litigating patents, trademarks, and other intellectual property assets, often in a multijurisdictional context. In addition, Wolfgang and his team frequently clear trademarks and ad campaigns and prosecute trademark applications.Wolfgang is co-editor of our <a href="https://lifesciences.mofo.com/" dir="ltr" target="_blank">MoFo Life Sciences Blog</a>.Wolfgang is a partner of Morrison Foerster based in our German office in Berlin.<h4 dir="ltr">Representative Transactional Matters</h4><ul dir="ltr"><li>U.S.-based BioTech fund on various research collaborations and other transactions with German start-ups or research institutions.</li><li>Negotiate multimillion-euro strategic partnership between two AgTech leaders concerning the development and commercialization of pest-resistant transgenic traits.</li><li>Craft global multi-million collaboration regarding AI-powered robotic MaaS.</li><li>UK-based digital health company on product roll-out of RPM applications in Germany; coordinate international expansion.</li><li>Advise numerous German start-ups on their U.S. expansion.</li><li>Draft diverse global patent and know-how license agreements in the areas of AgTech and life sciences—including gene licensing and licensing of genome editing technologies (e.g., CRISPR)—and medical devices; draft correlating supply and distribution agreements.</li><li>Structure IP rights in connection with joint ventures and other M&A transactions, including the carve-out of a patent-heavy semiconductor business comprising nine jurisdictions into a German–U.S. joint venture.</li><li dir="ltr">Advise on government-funded, multi-partner R&D projects and/or R&D projects with academic institutions, as well as on employee invention law.</li></ul><h4 dir="ltr">Representative Contentious IP Matters</h4><ul dir="ltr"><li>Lead counsel in an international dispute between software companies on rights and obligations following termination of a multi-million licensing and distribution arrangement; coordinate local counsel.</li><li>Defend Japanese chip manufacturer in high-profile SEP-litigation; advise on global settlement arrangement.</li><li>Represent leading internet retailer in patent infringement and concurrent invalidity proceedings in the context of a multijurisdictional dispute concerning touch-screen technology.</li><li>Represent diverse pharmaceutical and life sciences companies in national and cross-border patent infringement proceedings and other technology-related disputes, including preliminary injunctions as well as pre‑trial discovery and inspection proceedings, concerning, inter alia, drug-coating technologies, toner cartridges, medicinal gases, transgenic plants, and vaccines.</li><li dir="ltr">Conduct trademark, copyright, and false advertising litigation, including, most notably, a high-profile trademark litigation for a German bank, and a nine-country cross-border trademark litigation involving a famous pharmaceutical brand.</li></ul>

Dr. Wolfgang Schönig berät Mandanten zum Schutz des geistigen Eigentums sowohl in streitigen als auch nichtstreitigen Angelegenheiten, mit einem besonderen Fokus auf die IP-rechtliche Beratung von Unternehmen aus den Bereichen Life Sciences, Digitalwirtschaft und High-Tech.Zu seinen Beratungsschwerpunkten gehören Forschungs- und Entwicklungskooperationen, komplexe Lizenzvertragsgestaltungen und andereTechnologie-Transaktionen, sowie Know-how-Schutz-Strategien und das Arbeitnehmererfinderrecht. Er verfügt zudem über umfangreiche Prozesserfahrung zur Durchsetzung von Patenten, Marken- und anderen IP-Rechten, häufig auch in einem grenzüberschreitenden Kontext. Darüber hinaus bewertet Dr. Wolfgang Schönig zusammen mit seinem Team regelmäßig die Rechtskonformität von Werbekampagnen, prüft die Verfügbarkeit von Marken und entwickelt entsprechende Anmeldestrategien.Dr. Wolfgang Schönig ist Co-Editor des Life Sciences Blogs von Morrison Foerster.Er hat seinen Doktortitel an der Heinrich-Heine-Universität Düsseldorf und seinen LL.M. an der Fordham School of Law in New York erworben, jeweils mit Schwerpunkt im gewerblichen Rechtsschutz. Seinen MBA-Abschluss erhielt er von der European University in Barcelona.Dr. Wolfgang Schönig ist Partner des deutschen Büros von Morrison Foerster in Berlin.<h4 dir="ltr">Repräsentative Transaktionen</h4><ul><li dir="ltr">BioTech-Fonds mit Sitz in den USA bei verschiedenen Forschungskooperationen und anderen Transaktionen mit deutschen Start-ups oder Forschungseinrichtungen</li><li dir="ltr">Verhandlung einer hochvolumigen strategischen Partnerschaft zwischen zwei Technologieführern im Bereich Crop Science betreffend der Entwicklung und Kommerzialisierung von bestimmten Resistenzen</li><li dir="ltr">Gestaltung einer globalen Multi-Millionen-Zusammenarbeit für KI-gestützte Roboter</li><li dir="ltr">Ein in Großbritannien ansässiges Unternehmen für digitale Gesundheit bei der Markteinführung von RPM-Anwendungen in Deutschland; Koordination der internationalen Expansion</li><li dir="ltr">Beratung zahlreicher deutscher Start-ups bei deren US-Expansion</li><li dir="ltr">Strukturierung und Verhandlung diverser Patent- und Know-How-Lizenzen in den Bereichen Ag Tech und Life Sciences – einschließlich Fragen zu genome editing wie CRISPR-Medizingeräte, sowie Gestaltung von Softwarelizenzverträgen und Erstellung korrespondierender Liefer- und Vertriebsvereinbarungen</li><li dir="ltr">Strukturierung von IP-Rechten im Zusammenhang mit Joint Ventures und anderen M&A-Transaktionen, darunter die Koordination eines neun Länder umfassenden IP Carve-Outs im Rahmen eines Halbleiter-JVs</li><li dir="ltr">Beratung zu staatlich geförderten Multi-Partner F&E-Projekten und/oder F&E-Projekten mit akademischen Einrichtungen sowie zum Arbeitnehmererfindungsrecht</li></ul><h4 dir="ltr">Repräsentative IP-Streitigkeiten</h4><ul><li dir="ltr">Lead Counsel in einem internationalen Rechtsstreit zwischen Softwareunternehmen über Rechte und Pflichten nach Beendigung einer millionenschweren Lizenz- und Vertriebsvereinbarung; Koordinierung der lokalen Anwälte</li><li dir="ltr">Verteidigung eines japanischen Chipherstellers in einem prominenten Streitfall bezüglich Standardessentieller Patente (SEP); Beratung zu einer globalen Vergleichsregelung</li><li dir="ltr">Verteidigung einer führenden Internetplattform gegen Ansprüche eines Patenttrolls wegen vermeintlicher Verletzung von Patent bezüglich Touchscreen-Technologie sowie korrespondierende US-Streitigkeiten</li><li dir="ltr">Vertretung von Pharma- und Life Science-Unternehmen in nationalen und grenzüberschreitenden Patentverletzungsverfahren und anderen technologiebezogenen Streitigkeiten, einschließlich einstweiliger Verfügungsverfahren und vorprozessualer Beweissicherungs- und Besichtigungsverfahren, unter anderem in Bezug auf Arzneimittelbeschichtungstechnologien, Tonerkartuschen, medizinische Gase, transgene Pflanzen und Impfstoffe</li></ul>

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Wolfgang Schönig is a partner of Morrison Foerster (International) LLP based in our German office in Berlin. His practice specialty is advising on IP aspects relative to transactions in, among others, the life sciences, AgTech and pharmaceuticals industry, as well as legal representation in IP disputes. Wolfgang is specialized in research and development collaborations, complex licensing agreements, technology acquisitions, know-how protection strategies, and employee invention law.

Wolfgang Schönig focuses his practice on IP aspects relative to transactions in the digital, telecoms, high tech, life sciences, and pharmaceuticals industry, as well as legal representation in IP disputes.

Wolfgang Schönig advises clients on IP aspects relating to transactions in, among others, the life sciences, AgTech and pharmaceuticals industry, as well as legal representation in IP disputes. His additional focus areas include research and development collaborations, complex licensing agreements, technology acquisitions, know-how protection strategies, and employee invention law.

Wolfgang Schönig

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Brigid Bondoc and Wendy Chow authored an article for HealthTech about what the recent growth of generative artificial intelligence (AI) tools means for providers and patients.“In the absence of a clear legal and ethical framework that promotes the complete scope of the benefits of generative AI across the healthcare ecosystem, the full potential of the technology cannot be attained,” the authors wrote.They added: “AI healthcare applications today are cautiously serving in the administrative and supportive roles of the delivery system. But the true potential lies in the shift of technological tools from assistive to primary instruments of medical care. To integrate those tools into the healthcare environment, there must be a framework that addresses hard ethical and legal questions. Who will be practicing medicine: the doctor or the machine? What role should governments play in ensuring safety, impartiality and accountability?”Read the <a href="https://healthtechmagazine.net/article/2023/07/5-questions-generative-ai-in-healthcare-perfcon" target="_blank">full article</a>.

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Brigid is a food, drug, and medical device lawyer who counsels life sciences companies on a wide range of U.S. Food & Drug Administration pre- and post-market regulatory issues.

Brigid DeCoursey Bondoc

Brigid is a food, drug, and medical device lawyer who counsels life sciences companies on a wide range of U.S. Food & Drug Administration pre- and post-market regulatory issues.

Brigid Bondoc

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Patenting Artificial Intelligence in the U.S. – Considerations for AI Companies

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“In the absence of a clear legal and ethical framework that promotes the complete scope of the benefits of generative AI across the healthcare ecosystem, the full potential of the technology cannot be attained,” the authors wrote.

5 Questions About Generative AI in Healthcare

Morrison Foerster sponsored and spoke at the Association of Corporate Counsel’s 2023 Life Sciences Conference on May 11, 2023, on the topic of “Artificial Intelligence in Life Sciences: The Evolving Legal and Regulatory Landscape.” Eric Elting of TransPerfect Legal Solutions reported on the discussion. This article originally appeared on JDSupra and is reprinted with permission.In the last ten years, more data and greater computing power have led to a boom in AI-related patent applications, with life and medical sciences emerging as a top application field. In 2021, more than 100 applications submitted to the US Food and Drug Administration contained artificial intelligence and machine learning (AI/ML) components.According to Brigid Bondoc, Partner at Morrison Foerster in Washington, DC, the FDA shares an industry optimism that "AI/ML can help bring safe, effective, and high-quality treatments to patients faster," to quote Patrizia Cavazzoni, M.D., the director of the Center for Drug Evaluation and Research (CDER) at the FDA.How the agency evaluates and manages emerging AI/ML technologies in drug and biologic applications were addressed in the panel "Artificial Intelligence in Life Sciences: The Evolving Legal and Regulatory Landscape," presented by Morrison Foerster (MoFo), on May 11, 2023, at the Association for Corporate Counsel (ACC) live CLE event "2023 Life Sciences Conference: Future-Ready Resilience: Preparing to Face Challenges in the Uncertain Future."The panel, consisting of Bondoc; Anna Yuan, Senior Associate at MoFo; Wendy Chow, counsel to MoFo; and Lauren Wu, Head of Privacy, Sr. Dir. of Legal for Regulatory and Compliance, US Privacy Officer, and Global DPO at Evidation Health, discussed the evolving FDA policies and regulation on AI, data privacy implications, and protecting AI technology in life sciences.FDA RegulatoryBondoc presented the big picture on the FDA's AI regulation, where the agency actively monitors AI/ML software in medical devices and clinical developments. The agency may expand coverage into FDA-regulated activities, such as automation and learning of medical devices, diagnostic and therapeutic development efficiency, regulatory assessment, and post-market surveillance.The 21st Century Cures Act excludes five types of software from the FDA's medical device definition and regulation, including administrative support software, wellness, and electronic patient records. Still, the act allows the FDA to "claw back any exception if it identifies risks associated with the software as a medical device," said Wu. With AI technologies, wellness, and electronic records, "the waters get muddier," commented Wu—for example, what to do with a pedometer that monitors sleep and blood pressure. It may become more than a wellness device and move into the regulated medical device space.The FDA appears flexible and careful in developing an AI/ML regulation framework, offsetting the need to facilitate innovation while protecting public health. For example, Bondoc pointed to the agency's drafted guidance on Predetermined Change Control Plans (PCCP) for AI/ML-enabled device software functions, attempting to address a significant issue facing emerging technology.Historically, changes to a cleared or approved medical device that could significantly affect the safety or effectiveness of the device required submitting a premarket notification 510(k). Yet, the notification program is "fundamentally at odds with the use of AI/ML in medical devices that facilitate continual improvements and modifications to the device based on data gathered while in use," said Bondoc. The FDA aims to develop a regulatory approach tailored to AI/ML-enabled devices, allowing for safe and rapid modifications in response to new data while ensuring safety and effectiveness.The FDA sees ethical, privacy, and security issues using AI/ML in drug development. The agency is also concerned with the lack of transparency in algorithms that can lead to amplification errors or preexisting biases. "Although recognizing the problem, the potential regulatory solutions have yet to be provided," said Bondoc. Still, the agency aims to prevent algorithmic discrimination and advance the use of AI/ML techniques. It recently released a discussion paper, "Using Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products." And to further address issues of AI in drug manufacturing, the FDA released “Artificial Intelligence in Drug Manufacturing.”Protecting AI Technology in Life SciencesYuan confirmed an eight-fold increase in AI patents from 2017 to 2020, citing WIPO Technology Trends 2019: Artificial Intelligence and Mondaq, and observed a shift in the types of things patented. In the past, patents were more theoretical, such as novel AI algorithms for speech recognition. Nowadays, there are more patents on concrete, practical applications of machine learning in drug discovery and medical diagnostics.Patent applications include novel, practical applications of AI algorithms, drugs developed by AI, and computer hardware configurations and optimizations to facilitate AI training and inference processes. The patents fall into two buckets: one, the application of known AI to specific fields and sectors, and two, new AI models and algorithms. Yuan said the first is generally a higher value in patenting with broader and more detectable claims vis-à-vis the second with narrow and mathematically oriented claims.Privacy and AIThe use of AI/ML in life sciences involves data from individuals, even protected health information (PHI). At some point, whether it is the process's training input, inference, or output, the data use may run afoul of state privacy laws, implicate new legislation regulating AI and automated decision-making, or trigger federal law, including the Health Insurance Portability and Accountability Act (HIPAA) or the Federal Trade Commission Act prohibiting unfair and deceptive practices, such as the sale and use of racially biased programs.Chow also detailed the states with comprehensive privacy laws, including California, Virginia, and Tennessee, with numerous state laws pending in Connecticut, Indiana, Iowa, Montana, and Washington. State legislatures complement privacy laws with laws regulating AI and automated decision-making, such as the New York AI Bias Law. Still, Wu put privacy legislation into context: "It's not necessarily the driver for AI; it's a stakeholder."

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Of Counsel

Anna Yuan’s practice focuses on patent prosecution, IP due diligence, post-grant proceedings, and IP litigation support. 

Anna Yuan

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Morrison Foerster sponsored and spoke at the Association of Corporate Counsel’s 2023 Life Sciences Conference on May 11, 2023, on the topic of “Artificial Intelligence in Life Sciences: The Evolving Legal and Regulatory Landscape.” Eric Elting of TransPerfect Legal Solutions reported on the discussion. This article originally appeared on JDSupra and is reprinted with permission.

The Legal and Regulatory Landscape Evolving with AI in Life Sciences

On Wednesday, May 10, 2023, the Food and Drug Administration (FDA) <a href="https://www.federalregister.gov/public-inspection/2023-09985/using-artificial-intelligence-and-machine-learning-in-the-development-of-drug-and-biological" target="_blank">announced</a> the publication of a new discussion paper titled “<a href="https://www.fda.gov/media/167973/download" target="_blank">Using Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products</a>.” The discussion paper aims to facilitate a discussion with stakeholders on the use of artificial intelligence and machine learning (AI/ML) in drug development, including the development of medical devices intended for use with drugs. The discussion paper covers three main topics: the landscape of current and potential uses of AI/ML; considerations for the use of AI/ML; and next steps and stakeholder engagement.Here are five key takeaways:1. FDA Acknowledges that AI/ML Applications Exist for Each Stage of Drug Development. AI/ML may have applications for use in all stages of drug development, from drug discovery to pharmaceutical manufacturing. AI/ML has been applied to real‑world data (RWD) and data from digital health technologies (DHTs) to support drug development.  The first section of the discussion paper summarizes the different ways in which AI/ML can be used in drug discovery, clinical and nonclinical research, postmarketing surveillance, and advanced pharmaceutical manufacturing. 2. FDA Is Growing Its Own Experience with AI/ML for Drug Development. In recent years, the FDA has seen a growth in drug and biological product submissions that reference AI/ML. In response, FDA has taken a number of actions, including establishment of the CDER AI Steering Committee, the Innovative Science and Technology Approaches for New Drugs (ISTAND) Pilot Program, and the Model‑Informed Drug Development (MIDD) Pilot Program. For postmarket safety surveillance, the CDER Sentinel System, CBER Biologics Effectiveness and Safety (BEST) system, and CDRH National Evaluation System for health Technology (NEST) efforts, are exploring AI/ML approaches to improve existing systems.3. FDA Understands Development of Standards and Practices for Use of AI/ML Is Critical. The U.S. government and international community have expressed an increased commitment to facilitating AI innovation and adoption. Regulators and standards organizations have developed and issued standards to facilitate the advancement of ethical AI. For example, in August 2019, the National Institute for Standards and Technology (NIST) released the “U.S. Leadership in AI: A Plan for Federal Engagement in Developing Technical Standards and Related Tools.” In addition, in October 2021, the FDA, Health Canada, and the UK’s Medicines and Healthcare products Regulatory Agency (MHRA) jointly published 10 guiding principles to inform the development of Good Machine Learning Practices (GMLP) for medical devices using AI/ML.4. FDA Has Identified Key Questions on AI/ML in Drug Development. The FDA aims to initiate a discussion with stakeholders on the following three key areas and provides specific questions to solicit feedback.Human-Led Governance, Accountability, and Transparency<ul><li>In what specific use cases or applications of AI/ML in drug development are there the greatest need for additional regulatory clarity?</li><li>In your experience, what are the main barriers and facilitators of transparency with AI/ML used during the drug development process (and in what context)?</li><li>How are pre-specification activities managed, and changes captured and monitored, to ensure the safe and effective use of AI/ML in drug development?</li></ul>Quality, Reliability, and Representativeness of Data<ul><li>What additional data considerations exist for AI/ML in the drug development process?</li><li>What are some of the key practices utilized by stakeholders to help ensure data privacy and security?</li><li>What processes are developers using for bias identification and management?</li></ul>Model Development, Performance, Monitoring, and Validation<ul><li>What practices and documentation are being used to inform and record data source selection and inclusion or exclusion criteria?</li><li>In what context of use are stakeholders addressing explainability, and how have you balanced considerations of performance and explainability?</li><li>What are some examples of current tools, processes, approaches, and best practices being used by stakeholders for: selecting model types and algorithms for a given use, determining when to use specific approaches for validating models and measuring performance in a given context, evaluating transparency and explainability, and increasing model transparency, etc.?</li></ul>5. FDA Wants Your Feedback. The FDA is soliciting feedback on the opportunities and challenges in using AI/ML in drug and medical device development. The FDA includes a series of questions for feedback in the discussion paper, and a workshop with stakeholders is planned to provide an opportunity for further engagement. <a href="https://www.regulations.gov/docket/FDA-2023-N-0743" target="_blank">Comments must be submitted by August 9, 2023 (Docket No. FDA-2023-N-0743). </a>

FDA on Use of AI/ML in Drug Development: Five Key Takeaways

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FDA on Use of AI/ML in Drug Development: Five Key Takeaways

Recent Digital Health-Related FDA Announcements

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On Wednesday, May 10, 2023, the Food and Drug Administration (FDA) announced the publication of a new discussion paper titled “Using Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products.

While the traditional process of discovering a new drug is long and expensive, artificial intelligence drug development platforms can help reduce time and money by efficiently analyzing vast data sets within the drug discovery process. Researchers can use such AI platforms for identifying new biological targets associated with a particular disease, screening known compounds for new applications and designing new therapeutic compounds, among other applications.In this article, originally published in Law360, we explain the top 10 considerations for AI transactions involving use in drug discovery, which are:<ol><li>Consider the nature of the services being provided.</li><li>Be mindful of IP ownership.</li><li>Consider quality and privacy issues for training data.</li><li>Consider exclusivity.</li><li>Account for infringement risk.</li><li>Be reasonable with royalties.</li><li>Evaluate confidentiality and competitive use.</li><li>Plan for uncertainty.</li><li>Scrutinize contractual risk allocation.</li><li>Beware of an evolving regulatory landscape.</li></ol>For more detail on each of the considerations, read the <a href="https://media2.mofo.com/v3/assets/blt5775cc69c999c255/bltc9412c34b10121da/64499b26d57e320df0d8163a/230425-considerations-licensing-ai-drug-discovery.pdf">full article</a>.

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Stephanie assists companies with technology and life sciences transactions, representing both private and public companies, from emerging growth through the Fortune 50. In particular, Stephanie focuses on helping companies that leverage data through technology in structuring their business and commercial transactions.

Stephanie Sharron

Stephanie Sharron is partner in the Palo Alto office and a member of the firm’s Technology Transactions Group. Clients turn to Ms. Sharron for advice on how to leverage data through technology and the related issues that apply to their business models in light of the rapid changes in business, technology and law.

Stephanie Lynn Sharron

Wolfgang advises clients on contentious and non-contentious matters involving their most valuable intellectual property assets in the digital, high-tech, life sciences, and pharmaceutical industries.

10 Considerations When Licensing AI for Drug Discovery

While the traditional process of discovering a new drug is long and expensive, artificial intelligence drug development platforms can help reduce time and money by efficiently analyzing vast data sets within the drug discovery process.

10 Considerations When Licensing AI for Drug Discovery

On April 3, 2023, FDA released the draft guidance titled “<a href="https://www.fda.gov/media/166704/download" target="_blank">Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions</a>” (hereinafter “draft guidance”) proposing an approach to support iterative improvement through modifications to a machine learning device software function (ML-DSF) while continuing to provide reasonable assurance of device safety and effectiveness. FDA is accepting <a href="https://www.regulations.gov/docket/FDA-2022-D-2628" target="_blank">comments</a> on the draft guidance until July 3, 2023.Background:Historically, changes to an approved or cleared medical device that could significantly affect the safety or effectiveness of the device or that constitute a major change or modification in the intended use of the device required submission of a new 510(k). This framework is at odds with the use of artificial intelligence and machine learning in medical devices, which enable and encourage continuous improvements and modifications to the device based on information gathered during use. FDA has been engaged in the process of developing a regulatory approach tailored to AI/ML-enabled devices that allows for safe and rapid modifications in response to new data while ensuring safety and effectiveness.The idea of using a “predetermined change control plan” (PCCP) was first mentioned by FDA in its 2019 discussion paper entitled “<a href="https://www.fda.gov/media/122535/download" target="_blank">Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD)</a>” and has been widely discussed in the industry. In FDA’s <a href="https://www.fda.gov/media/145022/download" target="_blank">Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan</a> published in January 2021, FDA committed to publishing PCCP guidance. In addition, <a href="https://www.congress.gov/117/bills/hr2617/BILLS-117hr2617enr.pdf" target="_blank">Section 3308 of the Consolidated Appropriations Act</a>, also referred to as the Food and Drug Omnibus Reform Act (FDORA), added section 515C to the Food, Drug and Cosmetic Act, granting FDA authority to approve a PCCP “submitted in an application . . . that describes planned changes that may be made to the device . . . if the device remains safe and effective without any change.” In addition to FDA’s discussion of AI/ML-enabled devices, the White House released a <a href="https://www.whitehouse.gov/ostp/ai-bill-of-rights/" target="_blank">Blueprint for an AI Bill of Rights</a> on October 4, 2022, which discusses principles regarding the design, use, and deployment of automated systems.The draft guidance is applicable to ML-DSFs that the manufacturer “intends to modify over time” including those “for which modifications to the ML model are implemented automatically . . . as well as for ML-DSFs for which modifications to the ML model are implemented manually.”[1] The term “PCCP,” as used in the guidance, “refers to a plan that includes device modifications that would otherwise require a premarket approval supplement, De Novo submission, or a new premarket notification.”[2] Proactively pre-specifying and seeking market authorization for intended modifications to an ML-DSF in a PCCP removes the need to submit additional marketing submissions for each future modification that is delineated and implemented in accordance with the PCCP. Modifications to an ML-DSF that “could significantly affect, or that could affect, the safety or effectiveness of the device”[3] require premarket authorization, unless those modifications are covered by a PCCP. In addition, the modifications cannot change the intended use of the device or the benefit-risk profile.Elements of a PCCP:A PCCP is to be composed of three elements: (i) a Description of Modifications; (ii) a Modification Protocol; and (iii) an Impact Assessment.1. Description of ModificationsA PCCP must contain a detailed description of the specific, planned device modifications. The description of modifications includes three sub-elements: (i) an enumerated list of individual proposed device modifications; (ii) a specific rationale for the change to each part of the ML-DSF; and (iii) reference to the labeling changes associated with each modification. FDA recommends that a PCCP include specific modifications that can be verified and validated. These modifications should be “presented at a level of detail that permits understanding of the specific modifications that will be made to the ML-DSF.”[4] The Description of Modifications should clearly indicate whether the planned modifications will be implemented automatically or manually. FDA recognizes this is an evolving area and is proposing to consider PCCPs for ML-DSFs where modifications are implemented automatically to the extent FDA can “properly review them for substantial equivalence to the predicate or a reasonable assurance of safety and effectiveness.”[5] FDA reiterates that this subset of ML-DSFs is complex and will consider the benefit-risk assessment when reviewing PCCPs with automatically implemented modifications.Not all modifications may be appropriate for inclusion within a PCCP. Types of modifications that are acceptable for inclusion in a PCCP include:<ol><li>Modifications related to quantitative measures of ML-DSF performance specifications (e.g., improvements to analytical performance resulting from re-training the ML model using new data within the intended use population from the same type and range of input signal);</li><li>Modifications related to device inputs to the ML-DSF (e.g., expanding the algorithm to include new sources of the same type of signal); and</li><li>Limited modifications related to the device’s use and performance (e.g., for use within a specific subpopulation).</li></ol>FDA expects that modifications included in a PCCP should maintain the device within both the device’s intended use and indications for use.2. Modification ProtocolThe Modification Protocol describes the methodology used to develop, validate, and implement those modifications in a way that ensures the continued safety and effectiveness of the devices across relevant patient populations. FDA states that the draft guidance is consistent with the principles in the <a href="https://www.whitehouse.gov/ostp/ai-bill-of-rights/">Blueprint for an AI Bill of Rights</a>. The Modification Protocol should have four primary content elements, each with their own requirements:1. Data Management Practices – The Modification Protocol should include an outline of how data to support the proposed modifications will be collected, annotated, stored, retained, controlled, and used. The Modification Protocol should also clarify the relationship between the Modification Protocol data and data that is used to train and test both the initial and following versions of the ML-DSF. This is called the “data sequestration strategy” by FDA to ensure that training data sets and testing data sets are separated. Training data and testing data are sequestered to prevent overfitting and misquotes of test performance.The Modification Protocol should also include a description of the control methods used to prevent data or performance information leaking into the modification development process. In essence, the Modification Protocol should provide information that describes the protocols put in place to prevent access to performance testing data during the training and tuning process.Examples of the types of information to be included in a Modification Protocol describing data management practices are provided in Appendix A of the draft guidance.2. Re-training Practices – The Modification Protocol should include information about re-training practices, including the objective of re-training, a description of the ML model, the device components that may be modified, and triggers for re-training.3. Performance Evaluation – The Modification Protocol should discuss how the performance of the ML-DSF will be evaluated, including the triggers for evaluation, performance metrics to be computed, the statistical analysis plans that will be used to test performance for each modification, and confirmation that failures will be recorded and screened from implementation.4. Update Procedures – The Modification Protocol must include a description of how the devices will be updated to implement the modifications. This includes confirmation that either the verification and validation plans have not changed or that any changes are justified. Differences in performance and testing methods will be communicated to users. The PCCP should include a description of any labeling changes that will result, and the available labeling must include adequate directions for use and reflect information about the currently available versions of the ML-DSF, including information on site-specific modifications. Labeling should not reflect information on modifications that have not yet been implemented in the current version, as it could cause confusion and would be deemed misbranded. Examples of the types of information that should be submitted describing update procedures are also provided in Appendix A of the draft guidance.The PCCP should also clearly delineate which parts of the Modification Protocol are applicable to each modification. For a PCCP with multiple modifications, this may be achieved through a traceability table. The draft guidance includes a sample traceability table that provides an example of how a manufacturer can depict the traceability between the Description of Modifications and the Modification Protocol.3. Impact AssessmentThe draft guidance refers to the Impact Assessment as an assessment of the benefits and risks of the planned modifications and risk mitigations. There are five elements of an Impact Assessment:<ol><li>Comparison of the modified device to the unmodified version of the device;</li><li>Benefits-risk assessment for each individual modification;</li><li>Continued assurance of the safety and effectiveness of the device;</li><li>Impact of one modification on the implementation of another; and</li><li>Collective impact of implementing all modifications.</li></ol>Modifications to an authorized PCCP will generally constitute changes to the ML-DSF that require a new marketing submission for the device, including a modified PCCP. This is because modifications described in the PCCP include device changes that would typically require a PMA supplement, De Novo submission, or 510(k) premarket notification. FDA expects that the modified PCCP will need to be reviewed as part of the premarket review of the modified device because the modification will generally significantly affect the safety or effectiveness of the device. Where modifications to the PCCP are the only significant modifications since the prior device and PCCP authorization, FDA intends to focus review on the proposed PCCP.The draft guidance includes a decision tree[6] for determining whether a modification is within the scope of an authorized PCCP or if a new submission is required:<div data-reactroot=""><img src="https://media2.mofo.com/v3/images/blt5775cc69c999c255/blt4f5734e3eb37046b/6449a8834205ff10fc2438e4/230426-fda-ai_image.png?format=auto&amp;quality=60" alt="FDA "/></div>Referencing a PCCP in a Submission:FDA considers the PCCP to be part of the technological characteristics of the device. For devices subject to 510(k) requirements, the determination of substantial equivalence where the predicate device was authorized with a PCCP will be determined based on a comparison to the version of the predicate device cleared or approved prior to changes made under the PCCP.Example Scenarios of ML-DSFs That Employ PCCPs:Appendix B to the draft guidance provides examples that “illustrate different ML-DSF scenarios where a PCCP could be employed.”[7] For example, a particular ML-DSF analyzes images of skin lesions by identifying and characterizing features to aid in diagnosis. This ML-DSF was validated with a specific camera and is intended for use by a primary healthcare provider. The device was authorized with a PCCP. The pre-specified modification was for extension for use on additional general-purpose computing platforms, like smartphones and tablets. The general-purpose computing platform must include a 2D-camera that meets the minimum specifications in the PCCP, and the updated device must achieve a minimum performance defined in the Modification Protocol. In a scenario where the manufacturer implements a modification that was already specified in the PCCP and in conformance with the PCCP, the modification would not require a new marketing submission. On the other hand, if the manufacturer wanted to deploy a modified ML model that uses images captured by a thermographic camera, which was not specified in the PCCP, then a new marketing submission would be required because the modification could significantly affect the safety or effectiveness of the device and was not included in the PCCP.Appendix B also provides examples for patient monitoring software, ventilator settings software, an image acquisition assistance device, and feeding tube placement radiograph analysis software, with various hypothetical modification scenarios for each.Takeaways:This draft guidance is a significant step forward in FDA’s ability to effectively regulate AI/ML-enabled medical devices. The PCCP approach has the potential to save manufacturers from continuous submissions and to reduce the uncertainty around what support is required before making changes to devices with AI/ML components. However, as the draft guidance details, there are extensive requirements that manufacturers will need to satisfy in order to utilize the PCCP process. Depending on feedback from stakeholders, the final version of the guidance may be different from the current draft version.Device manufacturers developing ML-DSFs or incorporating them into their medical devices should consider submitting comments to the draft guidance. FDA recommends that the public submit comments to the draft guidance (<a href="https://www.regulations.gov/document/FDA-2022-D-2628-0002" target="_blank">Docket No. FDA-2022-D-2628</a>) within 90 days of publication in the Federal Register, by July 3, 2023. FDA hosted a <a href="https://www.fda.gov/medical-devices/workshops-conferences-medical-devices/webinar-marketing-submission-recommendations-predetermined-change-control-plan-artificial" target="_blank">public webinar</a> for medical device manufacturers and other stakeholders to discuss the draft guidance on April 13, 2023. Webinar materials, including the presentation slides and transcript, are available <a href="https://www.fda.gov/training-and-continuing-education/cdrh-learn" target="_blank">here</a>.Morrison Foerster continues to closely monitor developments regarding AI/ML and FDA’s regulation of medical devices. For questions or assistance, please contact the authors.[1] Draft guidance at 5.[2]Id. at 6.[3]Id. (citations omitted).[4]Id. at 16.[5]Id.[6]Id. at 15.[7]Id. at 8.

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Regulatory Analyst

Brandy Guarda is a regulatory analyst supporting the FDA + Healthcare Regulatory and Compliance practice in Morrison Foerster’s Washington, D.C. office. Brandy predominantly focuses on researching and tracking FDA and interagency regulatory guidance, proposed and final rules, legislation, and enforcement actions.

Brandy A. Guarda

Brandy Guarda

FDA Publishes Draft Guidance for Predetermined Change Control Plan for AI/ML-Enabled Device Software Functions

On April 3, 2023, FDA released the draft guidance titled “Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions” (hereinafter “draft guidance”) proposing an approach to support iterative improvement through modifications to a machine learning device software function (ML-DSF) while continuing to provide reasonable assurance of device safety and effectiveness. FDA is accepting comments on the draft guidance until July 3, 2023.

FDA Publishes Draft Guidance for Predetermined Change Control Plan for AI/ML-Enabled Device Software Functions

In September 2022, the U.S. Food & Drug Administration (FDA) released its Clinical Decision Support (CDS) Software Guidance and the final report on the Software Pre-Certification Pilot Program. The new FDA policies move away from prized flexibilities in the previous guidelines governing software products. In the new and final CDS Guidance, FDA modified its interpretation of each of the four criteria in section 520(o)(1)(E) of the Federal Food, Drug, and Cosmetic Act, significantly narrowing what FDA considers exempt from regulatory authority.Read the <a href="https://mofotech.mofo.com/topics/ai-trends-for-2023-fda-regulatory" target="_self">full blog post</a>.

AI Trends for 2023 - FDA Regulatory

In September 2022, the U.S. Food & Drug Administration (FDA) released its Clinical Decision Support (CDS) Software Guidance and the final report on the Software Pre-Certification Pilot Program.

AI Trends for 2023 - FDA Regulatory

The use of machine learning (ML) in the medical device field has greatly expanded in recent years, becoming increasingly important to the product offerings of many medical device companies. The benefits provided by ML are wide-ranging and include providing earlier and more accurate diagnoses by detecting patterns in clinical data associated with disease, improving surgical outcomes through preoperative and intraoperative surgical guidance, and enabling the development of personalized diagnostics and therapeutics. Medical device companies should look to build a robust patent portfolio for their ML inventions. This raises the question of how to build a patent portfolio for ML inventions in the medical device field. ML involves the training of mathematical models to draw conclusions from data, such as medical images or other clinically relevant data. While mathematical models by themselves are not eligible for patenting, their application to achieve practical results is patentable. Broadly speaking, patents for ML‑based inventions fit into three broad categories. <ul><li>Applications of ML. This category includes patents directed to novel results achieved using ML. Examples of ML applications in the medical device field include automatic identification of anatomical structures in imaging, automatic diagnoses from patient data, and patient recovery tracking. Patents in this category are often the most valuable, both because of their breadth of coverage and because infringing activity can be readily detected, often from advertising materials and use of the product. </li><li>Structure of the ML model. The structure of the ML model refers to the structure of the mathematical algorithm itself and how it processes data. Although mathematical algorithms by themselves are not eligible for patenting, the application of a novel ML model to a practical application can be patented. Patents in this category are often less valuable because their scope is narrower and detection of infringement is more difficult.</li><li>ML model training. This category includes patents directed to the process for training an ML model to achieve its purpose. Training of an ML model is critical to how well it performs and thus can be one of the most important aspects of developing an ML solution. However, infringement of patents in this category is often very difficult to detect because the process for training is typically not advertised or apparent from the use of the product.</li></ul>Although patents directed to the applications of ML are typically the most valuable, medical device companies should also pursue patents to ML model structure and training because these patents contribute to the benefits that a robust patent portfolio provides, including:<ul><li>Deterring others from practicing the invention. Patents, of course, can be used to prevent others from making and using an invention. However, patents that are unlikely to ever be asserted still serve as a deterrent from copying and as a roadblock to the ability of competitors to innovate their own products. </li><li>Providing freedom to operate. Filing a patent application ensures that an invention is in the public space and thereby cannot be patented by anyone that invents the same thing later. Preventing others that create the same invention at a later date from patenting the invention ensures a company’s ability to practice its own technology. This is an important countervailing consideration to keeping an ML invention as a trade secret since trade secret protections do not protect against independent invention and patenting.</li><li>Advertising to investors. This benefit is most relevant to emerging companies looking for investments or acquisitions. Patents can serve as advertisements of the technical capabilities and sophistication of a company. They provide investors with the ability to “look under the hood” and make their own independent assessment of the value of the underlying ML technology.</li></ul>Given the increasing importance of ML in the medical device market, robust patent portfolios protecting ML innovations can help ensure that medical device companies reap the benefits of their valuable innovations.

The Importance of Patents for ML-Based Medical Device Inventions

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Christopher Gloria is a partner in the firm’s Patent Group. Mr. Gloria’s practice concentrates on domestic and international patent prosecution, IP due diligence, and IP litigation support including patent infringement and validity determinations.

Christopher Gloria

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The Importance of Patents for ML-Based Medical Device Inventions

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The use of machine learning (ML) in the medical device field has greatly expanded in recent years, becoming increasingly important to the product offerings of many medical device companies.

FDA is actively monitoring the use of AI and machine learning (ML) software in medical devices and clinical developments, and has taken a number of first steps in building out a regulatory framework.  In 2021, FDA published the <a href="https://www.fda.gov/media/145022/download" target="_blank">AI and Machine Learning Software as a Medical Device Action Plan</a>, which outlines FDA’s approach to regulating AI/ML technologies and next steps in furthering oversight, as well as the <a href="https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles" target="_blank">Good Machine Learning Practice for Medical Device Development: Guiding Principles</a>, to inform the development of Good Machine Learning Practice.  FDA also released a <a href="https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices" target="_blank">database</a> of AI/ML-enabled medical devices currently marketed in the United States and will update this list as FDA reviews and authorizes additional AI/ML-enabled medical devices going forward. <a href="https://mofotech.mofo.com/topics/ai-trends-2022-fda.html" target="_self">Read more</a>.