Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms.

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure-activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein-ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.JK, MJW, JT, PJB, AB and RCG thank Unilever for funding

Review of QSAR Models and Software Tools for predicting Biokinetic Properties

In the assessment of industrial chemicals, cosmetic ingredients, and active substances in pesticides and biocides, metabolites and degradates are rarely tested for their toxicologcal effects in mammals. In the interests of animal welfare and cost-effectiveness, alternatives to animal testing are needed in the evaluation of these types of chemicals. In this report we review the current status of various types of in silico estimation methods for Absorption, Distribution, Metabolism and Excretion (ADME) properties, which are often important in discriminating between the toxicological profiles of parent compounds and their metabolites/degradation products. The review was performed in a broad sense, with emphasis on QSARs and rule-based approaches and their applicability to estimation of oral bioavailability, human intestinal absorption, blood-brain barrier penetration, plasma protein binding, metabolism and. This revealed a vast and rapidly growing literature and a range of software tools. While it is difficult to give firm conclusions on the applicability of such tools, it is clear that many have been developed with pharmaceutical applications in mind, and as such may not be applicable to other types of chemicals (this would require further research investigation). On the other hand, a range of predictive methodologies have been explored and found promising, so there is merit in pursuing their applicability in the assessment of other types of chemicals and products. Many of the software tools are not transparent in terms of their predictive algorithms or underlying datasets. However, the literature identifies a set of commonly used descriptors that have been found useful in ADME prediction, so further research and model development activities could be based on such studies.JRC.DG.I.6-Systems toxicolog

Predicting drug metabolism: experiment and/or computation?

Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.This is the accepted manuscript of a paper published in Nature Reviews Drug Discovery (Kirchmair J, Göller AH, Lang D, Kunze J, Testa B, Wilson ID, Glen RC, Schneider G, Nature Reviews Drug Discovery, 2015, 14, 387–404, doi:10.1038/nrd4581). The final version is available at http://dx.doi.org/10.1038/nrd458

Integration of In Silico and In Vitro ADMET properties in lead identification and optimization of compounds for the treatment of parasitic diseases

Parasitic infections are the major causes of illness and death in tropical regions especially in Africa. The main parasitic diseases include leishmaniasis, filariasis, malaria, river blindness, Chagas disease and schistosomiasis. With the absence of vaccines, treatment relies mainly on chemotherapy hence the need for efficacious and safe medicines. Many of the medicines currently used have low efficacy and cause side effects. Some are also being lost to drug resistance. To address the inadequacy of treatment options for infectious diseases, a number of initiatives have been started to promote drug discovery and development in Africa. In parallel they have been collaboration between African institutions and leading pharmaceutical companies as well as other relevant R & D organizations. This has led to the need to modernize African approaches to drug discovery and development with respect to the integration of medicinal chemistry, pharmacology and pharmacokinetics as reflected in the processes of Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET). However, scientific and technological expertise in pharmacokinetics for drug discovery is under developed in Africa

Exploring the effects of polymorphic variation on the stability and function of human cytochrome P450 enzymes in silico and in vitro

Includes bibliographical references.Cytochrome P450s are highly polymorphic enzymes responsible for the Phase I metabolism of over 80% of pharmaceutical drugs. Polymorphic variation can result in altered drug efficacy as well as adverse drug reactions so the lack of understanding of the effects of single amino acid substitutions on cytochrome P450 drug metabolism is a major problem for drug development. In order to begin to address this problem, this thesis describes an in silico analysis of over 300 nonsynonymous single nucleotide polymorphisms found across nine of the major human drug metabolising cytochrome P450 isoforms. Information from functional studies - in which regions of the cytochrome P450 structure important for substrate recognition, substrate and product access and egress and interaction with the cytochrome P450 reductase were delineated - was combined with in silico calculations on the effect of mutations on protein stability in order to establish the likely causes of altered drug metabolism observed for cytochrome P450 variants in functional assays carried out to date. This study revealed that 75% of all cytochrome P450 mutations showing altered activity in vitro are either predicted to be damaging to protein structure or are found within regions predicted to be important for catalytic activity. Furthermore, this study showed that 70% of the mutations that showed similar activity to the wild-type enzyme in in vitro studies lie outside of functional regions important for catalytic activity and are predicted to have no effect on protein stability. Based on these results, a cytochrome P450 polymorphic variant map was created that should find utility in predicting the functional effect of uncharacterised variants on drug metabolism. To further test the accuracy of the in silico predictions, in vitro assays were performed on a panel of CYP3A4 and CYP2C9 variants heterogeneously expressed in E.coli. All mutations predicted to alter protein function by stabilising or destabilising the apo-protein structure in silico were found to significantly alter the thermostability of the holo-protein in solution. Thermostability assays also suggest that other mutations may affect stability by disrupting haem binding, changing protein conformation or altering oligomer formation. The utility of a fluorescence-based functional P450 protein microarray platform, previously developed in our laboratory, for generating kinetic data for multiple CYP450 variants in parallel was also examined. Since the microarray platform in its current stage of development was found to be unsuitable for this purpose, kinetic data for the full panel of CYP3A4 and CYP2C9 variants was generated using solution phase assays, revealing several variants with altered catalytic turnover and/or binding affinity for fluorescent substrates

Integrative Systems Approaches Towards Brain Pharmacology and Polypharmacology

Polypharmacology is considered as the future of drug discovery and emerges as the next paradigm of drug discovery. The traditional drug design is primarily based on a “one target-one drug” paradigm. In polypharmacology, drug molecules always interact with multiple targets, and therefore it imposes new challenges in developing and designing new and effective drugs that are less toxic by eliminating the unexpected drug-target interactions. Although still in its infancy, the use of polypharmacology ideas appears to already have a remarkable impact on modern drug development. The current thesis is a detailed study on various pharmacology approaches at systems level to understand polypharmacology in complex brain and neurodegnerative disorders. The research work in this thesis focuses on the design and construction of a dedicated knowledge base for human brain pharmacology. This pharmacology knowledge base, referred to as the Human Brain Pharmacome (HBP) is a unique and comprehensive resource that aggregates data and knowledge around current drug treatments that are available for major brain and neurodegenerative disorders. The HBP knowledge base provides data at a single place for building models and supporting hypotheses. The HBP also incorporates new data obtained from similarity computations over drugs and proteins structures, which was analyzed from various aspects including network pharmacology and application of in-silico computational methods for the discovery of novel multi-target drug candidates. Computational tools and machine learning models were developed to characterize protein targets for their polypharmacological profiles and to distinguish indications specific or target specific drugs from other drugs. Systems pharmacology approaches towards drug property predictions provided a highly enriched compound library that was virtually screened against an array of network pharmacology based derived protein targets by combined docking and molecular dynamics simulation workflows. The developed approaches in this work resulted in the identification of novel multi-target drug candidates that are backed up by existing experimental knowledge, and propose repositioning of existing drugs, that are undergoing further experimental validations

Machine learning approach in pharmacokinetics and toxicity prediction

Ph.DDOCTOR OF PHILOSOPH

Molecular simulations on proteins of biomedical interest : A. Ligand-protein hydration B. Cytochrome P450 2D6 and 2C9 C. Myelin associated glycoprotein (MAG)

TOPIC 1: Water molecules mediating polar interactions in ligand–protein complexes contribute to both binding affinity and specificity. To account for such water molecules in computer-aided drug discovery, we performed an extensive search in the Cambridge Structural Database (CSD) to identify the geometrical criteria defining interactions of water molecules with ligand and protein. In addition, ab initio calculations were used to derive the propensity of ligand hydration. Based on these information we developed an algorithm (AcquaAlta) to reproduce water molecules bridging polar interactions between ligand and protein moieties. This approach was validated using 20 crystal structures and yielded a match of 76% between experimental and calculated water positions. The solvation algorithm was then applied to the docking of oligopeptides to the periplasmic oligopeptide binding protein A (OppA), supported by a pharmacophore-based alignment tool. TOPIC 2: Drug metabolism, toxicity, and interaction profile are major issues in the drug discovery and lead optimization processes. The Cytochromes P450 (CYPs) 2D6 and 2C9 are enzymes involved in the oxidative metabolism of a majority of the marketed drugs. By identifying the binding mode using pharmacophore pre-alignement and automated flexible docking, and quantifying the binding affinity by multi-dimensional QSAR, we validated a model family of 56 compounds (46 training, 10 test) and 85 (68 training, 17 test) for CYP2D6 and CYP2C9, respectively. The correlation with the experimental data (cross- validated r2 = 0.811 for CYP2D6 and 0.687 for CYP2C9) suggests that our approach is suited for predicting the binding affinity of compounds towards the CYP2D6 and CYP2C9. The models were challenged by Y-scrambling, and by testing an external dataset of binding compounds (15 compounds for CYP2D6 and 40 for CYP2C9) and not binding compounds (64 compounds for CYP2D6 and 56 for CYP2C9). TOPIC 3: After injury, neurites from mammalian adult central nervous systems are inhibited to regenerate by inhibitory proteins such as the myelin-associated glycoprotein (MAG). The block of MAG with potent glycomimetic antagonists could be a fruitful approach to enhance axon regeneration. Libraries of MAG antagonists were derived and synthesized starting from the (general) sialic acid moiety. The binding data were rationalized by docking studies, molecular dynamics simulations and free energy perturbations on a homology model of MAG. The pharmacokinetic profile (i.e. stability in cerebrospinal fluid, logD, and blood-brain barrier permeation) of these compounds has been thoroughly investigated to evaluate the drug-likeness of the identified antagonists