Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

Abstract

The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.

Anti‐tuberculosis effect of isoniazid scales accurately from zebrafish to humans

“…An exposure–response relationship of a drug is often assumed to be conserved between vertebrates (Bartelink et al, 2017; De Groote et al, 2011; Van Wijk, Ayoun Alsoud, et al, 2020; Wicha et al, 2018). As a proof of concept for the translation of the quantified exposure–response relationship in zebrafish larvae to humans, the relationship for isoniazid in zebrafish larvae was translated to humans, assuming the isoniazid response of M. tuberculosis in humans to be similar to the isoniazid response of its close relative M. marinum in the larvae.…”

“…An exposure-response relationship of a drug is often assumed to be conserved between vertebrates (Bartelink et al, 2017;De Groote et al, 2011;Van Wijk, Ayoun Alsoud, et al, 2020;Wicha et al, 2018).…”

“…Development of anti-TB drugs is especially difficult, because of the laboratory biosafety issues (World Health Organization, 2012), the slow replication rate of M. tuberculosis, and the long duration of treatment and patient follow-up (Ginsberg & Spigelman, 2007). As a result, there is a clear and urgent need for innovations in the development of new TB treatments (Ginsberg & Spigelman, 2007;Van Wijk, Ayoun Alsoud, Lennernäs, & Simonsson, 2020). the zebrafish larva as model organism (Schulthess et al, 2018).…”

Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases

“…Given the advantages these ML modelling methods provide, they are being increasingly used to design, discover, and optimize drugs for NTDs (Scotti et al, 2015). In particular, the application of these computational techniques to discover drugs for malaria, tuberculosis, trypanosomiasis, and leishmaniasis has been reviewed by Njogu et al (2016), for leishmaniasis and trypanosomiasis by Halder et al (2020), and for tuberculosis by van Wijk et al (2020) Case Studies Using AI and ML to Discover New Drugs for NTDs…”

Editorial on Special Issue “Tuberculosis Drug Discovery and Development 2019”

“…Three papers describe state-of-the-art approaches to TB drug discovery. The first one by van Wijk and co-authors deals with quantitative pharmacology models including machine learning and artificial intelligence [18]; the second one by Bruch and colleagues discusses structure-and target-based approaches to TB drug design [19]; the last one explores the -omics technologies and how they have been exploited so far in TB drug discovery [20].…”