AbstractWe introduce the concept of optimal drug design (ODD) as the use of an AI framework to optimize the exposure, safety, and efficacy of drugs. To exemplify the concept of ODD, we developed an artificial intelligence framework that integrates de novo molecular design, quantitative structure activity relationships, and pharmacokinetic-pharmacodynamic modeling. Specifically, our computational architecture has integrated a generative algorithm for small molecule design with a hybrid physiologically-based pharmacokinetic machine learning (PBPK-ML) model, which was applied to generate and optimize drug candidates for enhanced brain exposure. Publicly sourced data on the plasma and brain pharmacokinetics of 77 small molecule drugs in rats was used for model development. We have observed an approximate 30-fold and 120-fold increase on average in predicted brain exposure for AI generated molecules compared to known central nervous system drugs and randomly selected small organic molecules. We believe that with additional data and mechanistic modeling this in silico pipeline could facilitate the discovery of a new wave of optimally designed medicines for the treatment of CNS diseases.Graphical AbstractArtificial Intelligence Framework for the Optimization of Brain Pharmacokinetics.A genetic algorithm consisting of cross-breeding, mutating, scoring, and refining was used for de novo generation of a population of new molecular structures. SELFIE representations of molecules were used as input to a variational autoencoder for de novo generation/refinement of individual drug candidates. Molecular descriptors of individual drug candidates are generated and used as input into a trained neural network to generate drug-specific pharmacokinetic (PK) parameters. PK parameters are used as input into a physiologically-based pharmacokinetic (PBPK) model of the brain to predict brain PK of the drug candidate. Brain concentration-time profiles are integrated to obtain an area-under the curve (AUC), a metric of brain exposure, which is used to score and inform the design of new generations of molecules. Iterations of this framework generate novel drug candidates optimized for greater brain exposure. Created with BioRender.

Purpose: The purpose of this study was to assess the association of baseline tumor size (BTS) with other baseline clinical factors and outcomes in pembrolizumab-treated patients with advanced melanoma in KEYNOTE-001 (NCT01295827).Experimental Design: BTS was quantified by adding the sum of the longest dimensions of all measurable baseline target lesions. BTS as a dichotomous and continuous variable was evaluated with other baseline factors using logistic regression for objective response rate (ORR) and Cox regression for overall survival (OS). Nominal P values with no multiplicity adjustment describe the strength of observed associations.Results: Per central review by RECIST v1.1, 583 of 655 patients had baseline measurable disease and were included in this post hoc analysis. Median BTS was 10.2 cm (range, 1-89.5). Larger median BTS was associated with Eastern Cooperative Oncology Group performance status 1, elevated lactate dehydrogenase (LDH), stage M1c disease, and liver metastases (with or without any other sites; all P ≤ 0.001). In univariate analyses, BTS below the median was associated with higher ORR (44% vs. 23%; P < 0.001) and improved OS (HR, 0.38; P < 0.001). In multivariate analyses, BTS below the median remained an independent prognostic marker of OS (P < 0.001) but not ORR. In 459 patients with available tumor programmed death ligand 1 (PD-L1) expression, BTS below the median and PD-L1-positive tumors were independently associated with higher ORR and longer OS.Conclusions: BTS is associated with many other baseline clinical factors but is also independently prognostic of survival in pembrolizumab-treated patients with advanced melanoma. Clin Cancer Res; 24(20); 4960-7. ©2018 AACR See related commentary by Warner and Postow, p. 4915.