Artificial Intelligence and Pharmacometrics: Time to Embrace, Capitalize, and Advance?

Artificial Intelligence and Machine Learning Applied at the Point of Care

“…There are many opportunities to combine the strengths of PMX and ML (Chaturvedula et al, 2019). PMX models are constructed with differential equations and can incorporate (semi-) mechanistic knowledge that are based on biological and pharmacological principles.…”

Application of Machine Learning in Drug Development and Regulation: Current Status and Future Potential

“…8 FDA scientists are also working on improving the exposure-response analyses by combining ML with causal inference tools (e.g., inverse-probability weighting and marginal structural model). 9 • Toxicity prediction: ML is being explored to model quantitative structure activity relationships and to predict drug toxicity. ML has also been used to assess the association between kinase inhibition and adverse reactions, based on meta-analyses of clinical trials data.…”

Accelerating Therapeutics for Opportunities in Medicine: A Paradigm Shift in Drug Discovery

“…As the complexity of biomedical research questions has increased, so too has the need to bring together expertise and resources from multiple disciplines and organizations (Cooke et al, 2015). Consequently, several articles by thought leaders have called for more collaboration in the drug development process (Altshuler et al, 2010;Dahlin et al, 2015;Alteri and Guizzaro, 2018;Takebe et al, 2018;Chaturvedula et al, 2019).…”

“…The promise of AI-driven drug design carries with it, several challenges-the need for appropriate datasets, ability to generate and test evolving biological hypotheses, multi-parameter optimization, reduction in design-make-test-analyze cycle times, and adaptability of research culture (Schneider et al, 2020). ATOM is tackling these challenges through the collaborative development of a preclinical, open-source, small-molecule drug discovery platform (Chaturvedula et al, 2019). The initial stages have focused on building computational infrastructure, curating preclinical data from both GSK and public sources, and creating and testing data-driven modeling capabilities.…”

“…As the complexity of biomedical research questions has increased, so too has the need to bring together expertise and resources from multiple disciplines and organizations ( Cooke et al., 2015 ). Consequently, several articles by thought leaders have called for more collaboration in the drug development process ( Altshuler et al., 2010 ; Dahlin et al., 2015 ; Alteri and Guizzaro, 2018 ; Takebe et al., 2018 ; Chaturvedula et al., 2019 ). Open innovation and open-source research strategies which emphasize the value of collaboration and use of both internal and external information, are creating the opportunity for the drug research and development industry to leverage know-how from across organizations ( Munos, 2006 ; Hunter and Stephens, 2010 ; Owens, 2016 ).…”