Bio: Boryeu Mao is an academic researcher. The author has contributed to research in topics: Pharmacophore & Quantitative structure–activity relationship. The author has an hindex of 6, co-authored 7 publications receiving 252 citations.
TL;DR: The importance of training set size and diversity in model development, the implementation of linear and neighborhood modeling approaches, and the use of in silico methods to predict potential clinical liabilities are discussed.
Abstract: Computational methods are increasingly used to streamline and enhance the lead discovery and optimization process. However, accurate prediction of absorption, distribution, metabolism and excretion (ADME) and adverse drug reactions (ADR) is often difficult, due to the complexity of underlying physiological mechanisms. Modeling approaches have been hampered by the lack of large, robust and standardized training datasets. In an extensive effort to build such a dataset, the BioPrint database was constructed by systematic profiling of nearly all drugs available on the market, as well as numerous reference compounds. The database is composed of several large datasets: compound structures and molecular descriptors, in vitro ADME and pharmacology profiles, and complementary clinical data including therapeutic use information, pharmacokinetics profiles and ADR profiles. These data have allowed the development of computational tools designed to integrate a program of computational chemistry into library design and lead development. Models based on chemical structure are strengthened by in vitro results that can be used as additional compound descriptors to predict complex in vivo endpoints. The BioPrint pharmacoinformatics platform represents a systematic effort to accelerate the process of drug discovery, improve quantitative structure-activity relationships and develop in vitro/in vivo associations. In this review, we will discuss the importance of training set size and diversity in model development, the implementation of linear and neighborhood modeling approaches, and the use of in silico methods to predict potential clinical liabilities.
TL;DR: This work focuses on the utilization of a fuzzy pharmacophore description of molecular similarity and specifically on the influence of fuzzy Pharmacophore pattern matching on the neighborhood behavior (NB) of the similarity scoring scheme.
Abstract: The similarity principle, stating that molecules of similar structure behave similarly, is an important concept in medicinal chemistry. A properly characterized and well-understood neighborhood behavior of the structural space versus the activity space is fundamental for the application of the similarity principle in computational chemistry. In this work we focus on the utilization of a fuzzy pharmacophore description of molecular similarity and specifically on the influence of fuzzy pharmacophore pattern matching on the neighborhood behavior (NB) of the similarity scoring scheme. NB is defined as a structure-activity relationship between the intermolecular distances/ dissimilarities in the pharmacophore fingerprint structure space and the corresponding activity differences, formally seen as intermolecular distances in the activity spaces. The latter are defined on hand of a wide variety of datasets on pharmacological and physico-chemical properties and property profiles. We also investigate the clustering behavior (CB), where the structure-activity relationship is described in terms of distance-derived associations of compounds into clusters via classical hierarchical clustering procedures. The neighborhood behavior and the cluster behavior provide alternative and complementary criteria for evaluating the pertinence of a molecular similarity metric.
TL;DR: The development and the testing of a multiple pharmacophore hypothesis (MPH) that is formulated as a conceptual extension of the traditional QSAR approach to modeling the promiscuous binding of a large variety of drugs to CYP3A4 is presented.
Abstract: We report the QSAR modeling of cytochrome P450 3A4 (CYP3A4) enzyme inhibition using four large data sets of in vitro data. These data sets consist of marketed drugs and drug-like compounds all tested in four assays measuring the inhibition of the metabolism of four different substrates by the CYP3A4 enzyme. The four probe substrates are benzyloxycoumarin, testosterone, benzyloxyresorufin, and midazolam. We first show that using state-of-the-art QSAR modeling approaches applied to only one of these four data sets does not lead to predictive models that would be useful for in silico filtering of chemical libraries. We then present the development and the testing of a multiple pharmacophore hypothesis (MPH) that is formulated as a conceptual extension of the traditional QSAR approach to modeling the promiscuous binding of a large variety of drugs to CYP3A4. In the simplest form, the MPH approach takes advantage of the multiple substrate data sets and identifies the binding of test compounds as either proximal or distal relative to that of a given substrate. Application of the approach to the in silico filtering of test compounds for potential inhibitors of CYP3A4 is also presented. In addition to an improvement in the QSAR modeling for the inhibition of CYP3A4, the results from this modeling approach provide structural insights into the drug-enzyme interactions. The existence of multiple inhibition data sets in the BioPrint database motivates the original development of the concept of a multiple pharmacophore hypothesis and provides a unique opportunity for formulating alternative strategies of QSAR modeling of the inhibition of the in vitro metabolism of CYP3A4.
TL;DR: An innovative approach has been developed to construct new GPCR focused libraries and was able to recognize not only "classical" templates but also original ones, allowing us to identify new G PCR chemotypes interacting with aminergic and peptidergic GPCRs.
Abstract: An innovative approach has been developed in order to construct new GPCR focused libraries. Experimental binding data generated in house from 1939 diverse drug and drug-like compounds on 40 GPCR targets were used to develop and validate a "global GPCR" QSAR model accounting for pharmacophore features related to a general GPCR-binding behavior. To this end, proprietary 3-D descriptors representing pharmacophore fingerprints of the various conformers of the molecules were used to encode compound structures in a numerical form. Statistical treatment of the data was based on two different approaches, linear regression and predictive neighborhood behavior, and synergy models relying on both these two independent approaches were also developed. The best QSAR model was selected on hand of its statistical parameters (R 2 , RMS) and percentage of correctly predicted compounds on a randomly chosen validation set (20% of the compounds). A diverse GPCR library of 2,400 compounds was prepared by applying the global QSAR model on compounds already synthesized in house, as well as on virtual combinatorial compounds which were then synthesized if predicted to be potential GPCR binders by the model. The set of building blocks used to build combinatorial libraries has been enriched in original "GPCR-like" monomers, specially designed for this purpose according to medicinal chemistry know-how and literature knowledge. To validate our approach, 240 compounds (10%) of this library were randomly chosen and tested on 21 different amine and peptide GPCRs, together with 720 combinatorial compounds from an in house diversity-based hit-seeking library, as a reference. The experimental results on these 960 compounds were analyzed after pooling the compounds into those predicted as GPCR-active vs. inactive (360 and 600 compounds respectively). The average hit rate was found to be 5.5 fold higher for the GPCR predicted compounds and, furthermore, the global QSAR model was able to recognize not only "classical" templates but also original ones, allowing us to identify new GPCR chemotypes interacting with aminergic and peptidergic GPCRs.
••19 May 2005
TL;DR: Analysis of recent trends reveals that the physical properties of molecules that are currently being synthesized in leading drug discovery companies differ significantly from those of recently discovered oral drugs and compounds in clinical development.
Abstract: The application of guidelines linked to the concept of drug-likeness, such as the 'rule of five', has gained wide acceptance as an approach to reduce attrition in drug discovery and development. However, despite this acceptance, analysis of recent trends reveals that the physical properties of molecules that are currently being synthesized in leading drug discovery companies differ significantly from those of recently discovered oral drugs and compounds in clinical development. The consequences of the marked increase in lipophilicity--the most important drug-like physical property--include a greater likelihood of lack of selectivity and attrition in drug development. Tackling the threat of compound-related toxicological attrition needs to move to the mainstream of medicinal chemistry decision-making.
TL;DR: Compared 3,665 US Food and Drug Administration (FDA)-approved and investigational drugs against hundreds of targets, defining each target by its ligands, chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations.
Abstract: Although drugs are intended to be selective, at least some bind to several physiological targets, explaining side effects and efficacy. Because many drug-target combinations exist, it would be useful to explore possible interactions computationally. Here we compared 3,665 US Food and Drug Administration (FDA)-approved and investigational drugs against hundreds of targets, defining each target by its ligands. Chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations. Thirty were tested experimentally, including the antagonism of the beta(1) receptor by the transporter inhibitor Prozac, the inhibition of the 5-hydroxytryptamine (5-HT) transporter by the ion channel drug Vadilex, and antagonism of the histamine H(4) receptor by the enzyme inhibitor Rescriptor. Overall, 23 new drug-target associations were confirmed, five of which were potent (<100 nM). The physiological relevance of one, the drug N,N-dimethyltryptamine (DMT) on serotonergic receptors, was confirmed in a knockout mouse. The chemical similarity approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs.
TL;DR: Applied to 746 marketed drugs, a network of 1018 side effect–driven drug-drug relations became apparent, 261 of which are formed by chemically dissimilar drugs from different therapeutic indications, hinting at new uses of marketed drugs.
Abstract: Targets for drugs have so far been predicted on the basis of molecular or cellular features, for example, by exploiting similarity in chemical structure or in activity across cell lines. We used phenotypic side-effect similarities to infer whether two drugs share a target. Applied to 746 marketed drugs, a network of 1018 side effect-driven drug-drug relations became apparent, 261 of which are formed by chemically dissimilar drugs from different therapeutic indications. We experimentally tested 20 of these unexpected drug-drug relations and validated 13 implied drug-target relations by in vitro binding assays, of which 11 reveal inhibition constants equal to less than 10 micromolar. Nine of these were tested and confirmed in cell assays, documenting the feasibility of using phenotypic information to infer molecular interactions and hinting at new uses of marketed drugs.
TL;DR: What are the best strategies for identifying small molecules that modulate biological targets?
Abstract: Despite over a century of applying organic synthesis to the search for drugs, we are still far from even a cursory examination of the vast number of possible small molecules that could be created. Indeed, a thorough examination of all 'chemical space' is practically impossible. Given this, what are the best strategies for identifying small molecules that modulate biological targets? And how might such strategies differ, depending on whether the primary goal is to understand biological systems or to develop potential drugs?
TL;DR: A public, computer‐readable side effect resource (SIDER) that connects 888 drugs to 1450 side effect terms and contains information on frequency in patients for one‐third of the drug–side effect pairs is developed.
Abstract: The molecular understanding of phenotypes caused by drugs in humans is essential for elucidating mechanisms of action and for developing personalized medicines. Side effects of drugs (also known as adverse drug reactions) are an important source of human phenotypic information, but so far research on this topic has been hampered by insufficient accessibility of data. Consequently, we have developed a public, computer-readable side effect resource (SIDER) that connects 888 drugs to 1450 side effect terms. It contains information on frequency in patients for one-third of the drug–side effect pairs. For 199 drugs, the side effect frequency of placebo administration could also be extracted. We illustrate the potential of SIDER with a number of analyses. The resource is freely available for academic research at http://sideeffects.embl.de.