Metaverse Law in Orange County Lawyer Magazine
The January 2025 edition of Orange County Lawyer magazine features an article written by Metaverse Law’s Lily Li. Read “AI and Machine Learning in Drug Development and Clinical Trials” below or in Orange County Lawyer magazine.
[Originally published as a Feature Article: AI and Machine Learning in Drug Development and Clinical Trials, by Lily Li, in Orange County Lawyer Magazine, January 2025, Vol. 67 No.1, page 28.] AI and Machine Learning in Drug Development and Clinical Trials by Lily Li In 2013, sleep medication zolpidem (Ambien, Ambien CR, and Edluar) swept headlines. Marie Claire reported on an alarming and suspicious rise in users experiencing irrational eating, gambling, and even “sleep-driving” while in a hypnotic trance—waking with no memories of their actions.[1] In several cases, women arrested and convicted for driving under the influence contested their convictions, arguing that they were not liable for these undisclosed drug-related side effects. At the same time, several clinical studies suggested that women metabolized zolpidem differently from men. By reviewing existing literature, Japanese researchers out of Shimane University identified 40% higher concentrations of zolpidem in women than men following use, and higher rates of visual hallucinations and sensory distortions.[2] The FDA released a safety advisory, warning users of the risks of “next-morning impairment” for the use of Ambien and related drugs.[3] In addition, the FDA took the unusual step of recommending a 50% cut in the dosage for women. When asked about the change, an FDA director told ABCNews.com: “The changes are different in women and men . . .We don’t understand why yet, but women are more susceptible to next-morning impairment.”[4] Yet, a decade later, the evidence supporting different zolpidem dosages for women and men is unclear.[5] In part, this is due to the lack of research surrounding sex differences in drug impact and drug treatment, as well as substantial gaps in the inclusion of women in clinical studies. From 1977 to 1993, FDA policy recommended excluding women of childbearing potential from Phase 1 and early Phase II drug trials.[6] Even after this policy was removed in 1993, industry fears remained with respect to drug interactions with pregnancy. This episode with zolpidem raised several concerns in the drug development and clinical trial process:
[Originally published as a Feature Article: AI and Machine Learning in Drug Development and Clinical Trials, by Lily Li, in Orange County Lawyer Magazine, January 2025, Vol. 67 No.1, page 28.] AI and Machine Learning in Drug Development and Clinical Trials by Lily Li In 2013, sleep medication zolpidem (Ambien, Ambien CR, and Edluar) swept headlines. Marie Claire reported on an alarming and suspicious rise in users experiencing irrational eating, gambling, and even “sleep-driving” while in a hypnotic trance—waking with no memories of their actions.[1] In several cases, women arrested and convicted for driving under the influence contested their convictions, arguing that they were not liable for these undisclosed drug-related side effects. At the same time, several clinical studies suggested that women metabolized zolpidem differently from men. By reviewing existing literature, Japanese researchers out of Shimane University identified 40% higher concentrations of zolpidem in women than men following use, and higher rates of visual hallucinations and sensory distortions.[2] The FDA released a safety advisory, warning users of the risks of “next-morning impairment” for the use of Ambien and related drugs.[3] In addition, the FDA took the unusual step of recommending a 50% cut in the dosage for women. When asked about the change, an FDA director told ABCNews.com: “The changes are different in women and men . . .We don’t understand why yet, but women are more susceptible to next-morning impairment.”[4] Yet, a decade later, the evidence supporting different zolpidem dosages for women and men is unclear.[5] In part, this is due to the lack of research surrounding sex differences in drug impact and drug treatment, as well as substantial gaps in the inclusion of women in clinical studies. From 1977 to 1993, FDA policy recommended excluding women of childbearing potential from Phase 1 and early Phase II drug trials.[6] Even after this policy was removed in 1993, industry fears remained with respect to drug interactions with pregnancy. This episode with zolpidem raised several concerns in the drug development and clinical trial process:
- How do we recruit representative candidates for drug trials?
- How do we ensure the quality and availability of datasets for clinical research?
- How do we measure potential impacts of drug dosing on different populations?
- What are the legal implications for failing to address appropriate drug doses?
- Recruitment. According to the FDA, “AI/ML is being used to mine vast amounts of data, such as data from clinical trial databases, trial announcements, social media, medical literature, registries, and structured and unstructured data in EHRs [electronic health records], which can be used to match individuals to trials (Harrer, 219 Shah, Antony, & Hu, 2019).” In this manner, researchers can combine huge quantities of publicly available data and individual health data from prior research to identify participants with certain medical conditions (or lack of adverse conditions) for investigational treatments. For zolpidem, the use of AI/ML may have been able to identify a much broader list of participants for initial clinical testing, making it easier to assess and identify adverse reactions.
- Selection and Stratification of Trial Participants. In addition to initial recruitment, AI/ ML has the capability improve intake, selection, and classification of clinical trial participants. Based on baseline characteristics selected by the researchers, such as prior clinical data, and vitals/labs taken during intake, predictive algorithms can help identify high-risk participants.[8] These groups can then be randomized and then subject to more strict monitoring protocols. In the case of zolpidem, alcohol use is associated with sometimes severe adverse effects from the drug, and so it would be beneficial to screen out candidates with a history of alcoholism or, on the flip side, assess drug interactions for this high-risk group with additional support, monitoring, or counseling.
- Dose/Dosing Regimen Optimization. AI/ML can be used to predict drug exposure for different populations based on factors such as weight, height, sex, and other characteristics that might impact drug metabolism. Based on prior drug exposure and response profiles for similar drugs and similar populations, AI/ML can help to narrow the dose/dosing regimen selected for a study. As noted by the FDA’s discussion paper, this can help optimize drug dosing “in special populations where there may be limited data (e.g., rare disease studies, pediatric and pregnant populations).” Based on this research, we can imagine future scenarios where AI/ML could have avoided zolpidem dosing concerns, where graduated and limited dosing was tested and applied to different sex, age, and metabolism categories to determine ideal dosing.
- Data Analysis. On a more intriguing level, the FDA AI discussion paper discussed the concept of creating “digital twins” of patients for clinical trials. Essentially, an AI version of the clinical participant is created, using the existing candidate’s electronic health records, vital signs, labs and other records. Researchers can assess how the digital twin would react under normal conditions using AI/ML modeling based on data gathered from similar individuals. This digital twin would then act as a substitute for a placebo candidate in a clinical trial, and act as a benchmark against the actual patient undergoing investigational treatment. For zolpidem, this could be used to assess candidates that already have underlying medical conditions such as anxiety, depression, or other confounding factors, to see whether an adverse effect from a trial is due to the investigational treatment or something that is likely to occur to the same individual from anxiety alone.
- Postmarketing Safety Surveillance. Finally, AI/ML can help detect and assess adverse events once the drug enters the market. This is not just limited to individual case safety reports (ICSR), required by regulators, but can include adverse events reported publicly on social media and the wider internet. This type of postmarketing safety surveillance could assist researchers and drug companies in identifying potential drug risks, prior to landing on primetime news.