Opportunities and Considerations in the Application of Artificial Intelligence to Pharmacokinetic Prediction

Can Machine Learning Overcome the 95% Failure Rate and Reality that Only 30% of Approved Cancer Drugs Meaningfully Extend Patient Survival?

Despite implementing hundreds of strategies, cancer drug development suffers from a 95% failure rate over 30 years, with only 30% of approved cancer drugs extending patient survival beyond 2.5 months. Adding more criteria without eliminating nonessential ones is impractical and may fall into the "survivorship bias" trap. Machine learning (ML) models may enhance efficiency by saving time and cost. Yet, they may not improve success rate without identifying the root causes of failure. We propose a "STAR-guided ML system" (structure-tissue/cell selectivity-activity relationship) to enhance success rate and efficiency by addressing three overlooked interdependent factors: potency/specificity to the on/off-targets determining efficacy in tumors at clinical doses, on/off-target-driven tissue/cell selectivity influencing adverse effects in the normal organs at clinical doses, and optimal clinical doses balancing efficacy/safety as determined by potency/specificity and tissue/cell selectivity. STAR-guided ML models can directly predict clinical dose/efficacy/safety from five features to design/select the best drugs, enhancing success and efficiency of cancer drug development.

Revolutionizing pharmacokinetics: the dawn of AI-powered analysis

This editorial explores how artificial intelligence (AI) is revolutionizing the science of pharmacokinetics (PK). It discusses the challenges of conventional PK analysis and how AI has transformed this area. It highlights the promise of artificial intelligence (AI) in predicting pharmacokinetic profiles from chemical structures and its application in several aspects of pharmacology, including dosage customization and drug interactions. Additionally, it emphasizes how important ethical issues and openness are to AI applications, especially when it comes to pharmacokinetic prediction and dataset adaptation. Future directions for AI in PK are discussed, with the creation of all-inclusive AI pharmacokinetics/pharmacometrics software being envisioned. Drug discovery and patient care could be transformed toward more individualized and effective healthcare solutions with the help of this software, which could handle tasks such as data cleaning, model selection, and regulatory report preparation. The editorial highlights the importance of AI in improving pharmaceutical sciences while urging caution and teamwork in navigating its possible uses in pharmacokinetics.

Physiological Dynamics in the Upper Gastrointestinal Tract and the Development of Gastrointestinal Absorption Models for the Immediate-Release Oral Dosage Forms in Healthy Adult Human

This review is a revisit of various oral drug absorption models developed in the past decades, focusing on how to incorporate the physiological dynamics in the upper gastrointestinal (GI) tract. For immediate-release oral drugs, GI absorption is a critical input of drug exposure and subsequent human body response, yet difficult to model largely due to the complex GI environment. One of the biggest hurdles lies at capturing the high within-subject variability (WSV) of bioavailability measures, which can be mechanistically explained by the GI physiological dynamics. A thorough summary of how GI dynamics is handled in the absorption models would promote the development of mechanism-based oral drug absorption models, aid in the design of clinical studies regarding dosing regimens and bioequivalence studies based on WSV, and advance the decision-making on formulation selection.

Multi-compound and drug-combination pharmacokinetic research on Chinese herbal medicines

Traditional medicine has provided a basis for health care and disease treatment to Chinese people for millennia, and herbal medicines are regulated as drug products in China. Chinese herbal medicines have two features. They normally possess very complex chemical composition. This makes the identification of the constituents that are together responsible for the therapeutic action of an herbal medicine challenging, because how to select compounds from an herbal medicine for pharmacodynamic study has been a big hurdle in such identification efforts. To this end, a multi-compound pharmacokinetic approach was established to identify potentially important compounds (bioavailable at the action loci with significant exposure levels after dosing an herbal medicine) and to characterize their pharmacokinetics and disposition. Another feature of Chinese herbal medicines is their typical use as or in combination therapies. Coadministration of complex natural products and conventional synthetic drugs is prevalent worldwide, even though it remains very controversial. Natural product–drug interactions have raised wide concerns about reduced drug efficacy or safety. However, growing evidence shows that incorporating Chinese herbal medicines into synthetic drug-based therapies delivers benefits in the treatment of many multifactorial diseases. To address this issue, a drug-combination pharmacokinetic approach was established to assess drug–drug interaction potential of herbal medicines and degree of pharmacokinetic compatibility for multi-herb combination and herbal medicine–synthetic drug combination therapies. In this review we describe the methodology, techniques, requirements, and applications of multi-compound and drug-combination pharmacokinetic research on Chinese herbal medicines and to discuss further development for these two types of pharmacokinetic research.