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Drug metabolism and pharmacokinetics (DMPK) is an important branch of pharmaceutical sciences. The nature of ADME (absorption, distribution, metabolism, excretion) and PK (pharmacokinetics) inquiries during drug discovery and development has evolved in recent years from being largely descriptive to seeking a more quantitative and mechanistic understanding of the fate of drug candidates in biological systems. Tremendous progress has been made in the past decade, not only in the characterization of physiochemical properties of drugs that influence their ADME, target organ exposure, and toxicity, but also in the identification of design principles that can minimize drug-drug interaction (DDI) potentials and reduce the attritions. The importance of membrane transporters in drug disposition, efficacy, and safety, as well as the interplay with metabolic processes, has been increasingly recognized. Dramatic increases in investments on new modalities beyond traditional small and large molecule drugs, such as peptides, oligonucleotides, and antibody-drug conjugates, necessitated further innovations in bioanalytical and experimental tools for the characterization of their ADME properties. In this review, we highlight some of the most notable advances in the last decade, and provide future perspectives on potential major breakthroughs and innovations in the translation of DMPK science in various stages of drug discovery and development. This review highlights some of the most notable advances in the translation of DMPK science in drug discovery and development in the last decade and predicts future breakthroughs and innovations. [Display omitted] [ABSTRACT FROM AUTHOR]

• Physiologically relevant cell-based assays, using data-driven AI/ML approaches, have become a cornerstone in developing better drugs. However, the value of the data generated is limited by the lack of sufficiently implemented and executed data standards. It is important to raise awareness of the critical role that data standards play in more effectively delivering on the promise of the digital age for developing treatments for patients. Each year, millions to trillions of data points are generated to evaluate the response of chemicals and biologicals to human cells in vitro and in vivo using various technologies and endpoints. Despite the vast amount of data available, the development process has not become significantly more efficient in recent years. Given the increasing use of more complex physiological models, which are time-consuming and significantly more expensive, it is crucial to maximize the value of these valuable data through improved standardization. [ABSTRACT FROM AUTHOR]