Prioritizing Hits with Appropriate Trade‐Offs Between HIV‐1 Reverse Transcriptase Inhibitory Efficacy and MT4 Blood Cells Toxicity Through Desirability‐Based Multiobjective Optimization and Ranking

Nonnucleoside reverse transcriptase (RT) inhibitors (NNRTIs) constitute a promising therapeutic option for AIDS. However, the emergence of virus‐NNRTIs resistance was found to be a major problem in the field. Toward that goal, a “knock‐out” strategy stands out between the several options to circumvent the problem. However the high drug or drug‐drug concentrations often used generate additional safety concerns. The need for approaches able to early integrate drug‐ or lead‐likeness, toxicity and bioavailability criteria in the drug discovery phase is an emergent issue. Given that, we propose a combined strategy based on desirability‐based multiobjective optimization (MOOP) and ranking for the prioritization of HIV‐1 NNRTIs hits with appropriate trade‐offs between inhibitory efficacy over the HIV‐1 RT and toxic effects over MT4 blood cells. Through the MOOP process, the theoretical levels of the predictive variables required to reach a desirable RT inhibitor candidate with the best possible compromise between efficacy and safety were found. This information is used as a pattern to rank the library of compounds according to a similarity‐based structural criterion, providing a ranking quality of 64 %/71 %/73 % in training/validation/test set. A comparative study between the sequential, parallel and multiobjective virtual screening revealed that the multiobjective approach can outperform the other approaches. These results suggest that the identification of NNRTIs hits with appropriate trade‐offs between potency and safety, rather than fully optimized hits solely based on potency, can facilitate the hit to lead transition and increase the likelihood of the candidate to evolve into a successful antiretroviral drug.

[1]  Han van de Waterbeemd,et al.  Chemometric Methods in Molecular Design: van de Waterbeemd/Chemometric , 1995 .

[2]  Ying Zhang,et al.  New drug candidates and therapeutic targets for tuberculosis therapy. , 2006, Drug discovery today.

[3]  Thomas F. Coleman,et al.  On the convergence of interior-reflective Newton methods for nonlinear minimization subject to bounds , 1994, Math. Program..

[4]  Simona Distinto,et al.  Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection—What can we learn from earlier mistakes? , 2008, J. Comput. Aided Mol. Des..

[5]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[6]  Bob Weinhold.  从长计议: 用什么作燃料? 柴油在美国 , 2003 .

[7]  R. W. Hansen,et al.  The price of innovation: new estimates of drug development costs. , 2003, Journal of health economics.

[8]  Christopher I. Bayly,et al.  Evaluating Virtual Screening Methods: Good and Bad Metrics for the "Early Recognition" Problem , 2007, J. Chem. Inf. Model..

[9]  Reinaldo Molina Ruiz,et al.  Desirability-based methods of multiobjective optimization and ranking for global QSAR studies. Filtering safe and potent drug candidates from combinatorial libraries. , 2008, Journal of combinatorial chemistry.

[10]  C. M. Reeves,et al.  Function minimization by conjugate gradients , 1964, Comput. J..

[11]  Hans-Jürgen Federsel,et al.  In search of sustainability: process R&D in light of current pharmaceutical industry challenges. , 2006, Drug discovery today.

[12]  David Hartsough,et al.  Toward an Optimal Procedure for Variable Selection and QSAR Model Building , 2001, J. Chem. Inf. Comput. Sci..

[13]  C. R. Deboor,et al.  A practical guide to splines , 1978 .

[14]  Charles J. Manly,et al.  The impact of informatics and computational chemistry on synthesis and screening. , 2001, Drug discovery today.

[15]  I Rebelo,et al.  Application of desirability-based multi(bi)-objective optimization in the design of selective arylpiperazine derivates for the 5-HT1A serotonin receptor. , 2009, European journal of medicinal chemistry.

[16]  B. Skagerberg,et al.  Predictive ability of regression models. Part I: Standard deviation of prediction errors (SDEP) , 1992 .

[17]  Valerie J Gillet,et al.  Multiobjective optimization in quantitative structure-activity relationships: deriving accurate and interpretable QSARs. , 2002, Journal of medicinal chemistry.

[18]  Robert Hooke,et al.  `` Direct Search'' Solution of Numerical and Statistical Problems , 1961, JACM.

[19]  Kimito Funatsu,et al.  GA Strategy for Variable Selection in QSAR Studies: Application of GA-Based Region Selection to a 3D-QSAR Study of Acetylcholinesterase Inhibitors , 1999, J. Chem. Inf. Comput. Sci..

[20]  D I Stuart,et al.  Structural basis for the resilience of efavirenz (DMP-266) to drug resistance mutations in HIV-1 reverse transcriptase. , 2000, Structure.

[21]  Xiayang Qiu,et al.  First X-ray cocrystal structure of a bacterial FabH condensing enzyme and a small molecule inhibitor achieved using rational design and homology modeling. , 2003, Journal of medicinal chemistry.

[22]  S. Weisberg.  Plots, transformations, and regression , 1985 .

[23]  G. Derringer,et al.  Simultaneous Optimization of Several Response Variables , 1980 .

[24]  Thomas F. Coleman,et al.  An Interior Trust Region Approach for Nonlinear Minimization Subject to Bounds , 1993, SIAM J. Optim..

[25]  S. Schenone,et al.  Design, synthesis, SAR, and molecular modeling studies of acylthiocarbamates: a novel series of potent non-nucleoside HIV-1 reverse transcriptase inhibitors structurally related to phenethylthiazolylthiourea derivatives. , 2003, Journal of medicinal chemistry.

[26]  Jürgen Drews,et al.  Innovation deficit revisited: reflections on the productivity of pharmaceutical R&D , 1998 .

[27]  Gisbert Schneider,et al.  Evaluation of Distance Metrics for Ligand‐Based Similarity Searching , 2004, Chembiochem : a European journal of chemical biology.

[28]  Jan Balzarini,et al.  Non-nucleoside HIV-1 reverse transcriptase inhibitors. Part 11: structural modulations of diaryltriazines with potent anti-HIV activity. , 2008, European journal of medicinal chemistry.

[29]  D. Butina,et al.  Predicting ADME properties in silico: methods and models. , 2002, Drug discovery today.

[30]  Anderson Coser Gaudio,et al.  BuildQSAR: A New Computer Program for QSAR Analysis , 2000 .

[31]  E. Clercq.  HIV resistance to reverse transcriptase inhibitors , 1994 .

[32]  R. Boggia,et al.  Genetic algorithms as a strategy for feature selection , 1992 .

[33]  A. Hagler,et al.  Chemoinformatics and Drug Discovery , 2002, Molecules : A Journal of Synthetic Chemistry and Natural Product Chemistry.

[34]  Valerie J Gillet,et al.  New directions in library design and analysis. , 2008, Current opinion in chemical biology.

[35]  J Drews.  Quo vadis, biotech? (Part 2). , 2001, Drug discovery today.

[36]  E. De Clercq,et al.  The HIV‐1 Reverse Transcription (RT) Process as Target for RT Inhibitors , 2000, Medicinal research reviews.

[37]  Obesity: A high dose of JuNK for fat mice , 2003, Nature Reviews Drug Discovery.

[38]  Hans-Joachim Böhm,et al.  A guide to drug discovery: Hit and lead generation: beyond high-throughput screening , 2003, Nature Reviews Drug Discovery.

[39]  Paola Gramatica,et al.  Introduction General Considerations , 2022 .

[40]  Angelo Carotti,et al.  Improving Quantitative Structure-Activity Relationships through Multiobjective Optimization , 2009, J. Chem. Inf. Model..

[41]  Joelle M. R. Gola,et al.  Focus on success: using a probabilistic approach to achieve an optimal balance of compound properties in drug discovery , 2006, Expert opinion on drug metabolism & toxicology.

[42]  Leandro Martínez,et al.  Molecular dynamics simulations of ligand dissociation from thyroid hormone receptors: evidence of the likeliest escape pathway and its implications for the design of novel ligands. , 2006, Journal of medicinal chemistry.

[43]  Denis M. Bayada,et al.  Molecular Diversity and Representativity in Chemical Databases , 1999, J. Chem. Inf. Comput. Sci..

[44]  R. Buckheit.  Non-nucleoside reverse transcriptase inhibitors: perspectives on novel therapeutic compounds and strategies for the treatment of HIV infection , 2001, Expert opinion on investigational drugs.

[45]  Jianping Ding,et al.  Review of HIV-1 reverse transcriptase three-dimensional structure : implications for drug design , 1993 .

[46]  T. Rakić,et al.  Chemometrical evaluation of ropinirole and its impurity's chromatographic behavior. , 2009, Journal of chromatography. A.

[47]  Jianping Ding,et al.  Locations of anti-AIDS drug binding sites and resistance mutations in the three-dimensional structure of HIV-1 reverse transcriptase. Implications for mechanisms of drug inhibition and resistance. , 1994, Journal of molecular biology.

[48]  J Desmyter,et al.  Rapid and automated tetrazolium-based colorimetric assay for the detection of anti-HIV compounds. , 1988, Journal of virological methods.

[49]  K. Weiss.  Toxin-binding treatment for Clostridium difficile: a review including reports of studies with tolevamer. , 2009, International journal of antimicrobial agents.