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Preface by Alan G. E. Wilson
The advent of computational modeling technologies, such as in silico, AI (Artificial Intelligence) and ML (Machine Learning), etc. are revolutionizing the science and application of predictive ADMET/PK in drug discovery and development (i.e., absorption, distribution, metabolism, and toxicology/pharmacokinetics).
Such technologies have the potential to fundamentally transform the application and impact of predictive ADMET/PK in support of drug discovery and development. Whilst this concept is not novel, these technologies have been rapidly advancing, a point indicated by the numerous publications in this area, and expertly discussed in this special edition of Xenobiotica, entitled “Predictive ADMET: Transforming Drug Development.” This special edition focuses on the status of these cutting-edge technologies, and their application in drug discovery and development.
This special edition covers a range of technological issues and topics, which are of strategic importance and relevance, to predictive ADMET/PK in support of drug discovery and development. It is not the intent, or indeed possible, to cover all areas of importance in predictive ADMET/PK. Rather, this publication has necessitated a strategic choice, which hoped will serve to inspire the future development, and application, and highlight the impact of predictive ADMET/PK.
Hopefully, the reader will find these articles on predictive ADMET/PK of interest, and illustrative of the potential capabilities and challenges of these predictive approaches and technologies; and continue to encourage and promote further interest, development, and progress of predictive ADMET/PK, in drug discovery and development. There is little doubt that predictive ADMET/PK will continue to grow and play an increasingly important role in drug discovery and approval.
I hope this special edition will serve to highlight the many opportunities and challenges that await in predictive ADMET/PK. There is little doubt that predictive technologies, such as in silico, AI, and ML, will be increasingly important as predictive ADMET/PK progresses.
I want to take this opportunity to express my sincere thanks to all the authors who contributed to this special edition of Xenobiotica. Hopefully these articles will serve to inspire and challenge the reader as predictive ADMET/PK advances. My thanks also go out to the scientists who critically review the various manuscripts for their approvability.
Special thanks are due to Professor Dennis Parke, the founder of Xenobiotica, and to the previous editors of Xenobiotica, and to my mentor and PhD advisor, Professor Jim Bridges; and to the many brilliant scientists and colleagues for their guidance and inspiration, as I traversed the wonders of ADMET/PK and predictive modeling.
Heartfelt thanks and my deep appreciation go out to my wife, Ayako Takei Wilson, for her support and encouragement. Thanks, also to Professor Costas Ioannides, the Editor-in-Chief of Xenobiotica, for his patience and persistence in the completion of this special edition.
Funding
The author(s) reported there is no funding associated with the work featured in this article.