Virtual chemical reactions for drug design

Two methods for the fast, fragment-based combinatorial molecule assembly were developed. The software COLIBREE® (Combinatorial Library Breeding) generates candidate structures from scratch, based on stochastic optimization [1]. Result structures of a COLIBREE design run are based on a fixed scaffold and variable linkers and side-chains. Linkers representing virtual chemical reactions and side-chain building blocks obtained from pseudo-retrosynthetic dissection of large compound databases are exchanged during optimization. The process of molecule design employs a discrete version of Particle Swarm Optimization (PSO) [2]. Assembled compounds are scored according to their similarity to known reference ligands. Distance to reference molecules is computed in the space of the topological pharmacophore descriptor CATS [3]. In a case study, the approach was applied to the de novo design of potential peroxisome proliferator-activated receptor (PPAR gamma) selective agonists. In a second approach, we developed the formal grammar Reaction-MQL [4] for the in silico representation and application of chemical reactions. Chemical transformation schemes are defined by functional groups participating in known organic reactions. The substructures are specified by the linear Molecular Query Language (MQL) [5]. The developed software package contains a parser for Reaction-MQL-expressions and enables users to design, test and virtually apply chemical reactions. The program has already been used to create combinatorial libraries for virtual screening studies. It was also applied in fragmentation studies with different sets of retrosynthetic reactions and various compound libraries

The efficiency of multi-target drugs: the network approach might help drug design

Despite considerable progress in genome- and proteome-based high-throughput screening methods and rational drug design, the number of successful single target drugs did not increase appreciably during the past decade. Network models suggest that partial inhibition of a surprisingly small number of targets can be more efficient than the complete inhibition of a single target. This and the success stories of multi-target drugs and combinatorial therapies led us to suggest that systematic drug design strategies should be directed against multiple targets. We propose that the final effect of partial, but multiple drug actions might often surpass that of complete drug action at a single target. The future success of this novel drug design paradigm will depend not only on a new generation of computer models to identify the correct multiple hits and their multi-fitting, low-affinity drug candidates but also on more efficient in vivo testing.Comment: 6 pages, 2 figures, 1 box, 38 reference

Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

Extending an alginate drug delivery experiment to teach computational modeling and engineering analysis to 1st year biomedical engineering students

Engaging biomedical engineering (BME) students in the first year has been an important part of The University of Texas at Austin’s strategy to improve student motivation, retention, and self-efficacy. First year engineering curricula across the country have increasingly included an introduction to engineering or design course in addition to core math and science courses. At UT Austin, a first-year design course and drug-delivery design class module has been previously described[1]. This course has since been expanded from 1 credit hour to 3 credit hours and the drug-delivery design module has been enhanced to include computational design and analysis using 2 different tools (Microsoft Excel and MATLAB). Previously, students analyzed their experimental data using simple curve fitting to determine the diffusivity constant. This paper describes the instruction of a Fick’s law-based computational simulation implemented in both Excel and MATLAB in order to match students’ experimental data. Students were able to use their simulation to solve for the diffusion coefficient and to estimate the amount of drug (a dye was used as a surrogate for a drug) lost in the drug delivery device loading process. In addition, students learned how to use both Excel and MATLAB for engineering analysis so that they will be prepared for future engineering courses. General Excel and MATLAB competencies were tested using low-stakes in-class quizzes and students’ attitudes were measured from end-of-semester course and instructor surveys. Students showed functional Excel and MATLAB knowledge and responded positively on course and instructor surveys.Cockrell School of Engineerin

Extended two-stage adaptive designswith three target responses forphase II clinical trials

We develop a nature-inspired stochastic population-based algorithm and call it discrete particle swarm optimization tofind extended two-stage adaptive optimal designs that allow three target response rates for the drug in a phase II trial.Our proposed designs include the celebrated Simon’s two-stage design and its extension that allows two target responserates to be specified for the drug. We show that discrete particle swarm optimization not only frequently outperformsgreedy algorithms, which are currently used to find such designs when there are only a few parameters; it is also capableof solving design problems posed here with more parameters that greedy algorithms cannot solve. In stage 1 of ourproposed designs, futility is quickly assessed and if there are sufficient responders to move to stage 2, one tests one ofthe three target response rates of the drug, subject to various user-specified testing error rates. Our designs aretherefore more flexible and interestingly, do not necessarily require larger expected sample size requirements thantwo-stage adaptive designs. Using a real adaptive trial for melanoma patients, we show our proposed design requires onehalf fewer subjects than the implemented design in the study

Extinction of cue-evoked drug-seeking relies on degrading hierarchical instrumental expectancies

There has long been need for a behavioural intervention that attenuates cue-evoked drug-seeking, but the optimal method remains obscure. To address this, we report three approaches to extinguish cue-evoked drug-seeking measured in a Pavlovian to instrumental transfer design, in non-treatment seeking adult smokers and alcohol drinkers. The results showed that the ability of a drug stimulus to transfer control over a separately trained drug-seeking response was not affected by the stimulus undergoing Pavlovian extinction training in experiment 1, but was abolished by the stimulus undergoing discriminative extinction training in experiment 2, and was abolished by explicit verbal instructions stating that the stimulus did not signal a more effective response-drug contingency in experiment 3. These data suggest that cue-evoked drug-seeking is mediated by a propositional hierarchical instrumental expectancy that the drug-seeking response is more likely to be rewarded in that stimulus. Methods which degraded this hierarchical expectancy were effective in the laboratory, and so may have therapeutic potential