Visual and computational analysis of structure-activity relationships in high-throughput screening data

Novel analytic methods are required to assimilate the large volumes of structural and bioassay data generated by combinatorial chemistry and high-throughput screening programmes in the pharmaceutical and agrochemical industries. This paper reviews recent work in visualisation and data mining that can be used to develop structure-activity relationships from such chemical/biological datasets

Data-mining of potential antitubercular activities from molecular ingredients of traditional Chinese medicines

Background. Traditional Chinese medicine encompasses a well established alternate system of medicine based on a broad range of herbal formulations and is practiced extensively in the region for the treatment of a wide variety of diseases. In recent years, several reports describe in depth studies of the molecular ingredients of traditional Chinese medicines on the biological activities including anti-bacterial activities. The availability of a well-curated dataset of molecular ingredients of traditional Chinese medicines and accurate in-silico cheminformatics models for data mining for antitubercular agents and computational filters to prioritize molecules has prompted us to search for potential hits from these datasets.Results. We used a consensus approach to predict molecules with potential antitubercular activities from a large dataset of molecular ingredients of traditional Chinese medicines available in the public domain. We further prioritized 160 molecules based on five computational filters (SMARTSfilter) so as to avoid potentially undesirable molecules. We further examined the molecules for permeability across Mycobacterial cell wall and for potential activities against non-replicating and drug tolerant Mycobacteria. Additional in-depth literature surveys for the reported antitubercular activities of the molecular ingredients and their sources were considered for drawing support to prioritization.Conclusions. Our analysis suggests that datasets of molecular ingredients of traditional Chinese medicines offer a new opportunity to mine for potential biological activities. In this report, we suggest a proof-of-concept methodology to prioritize molecules for further experimental assays using a variety of computational tools. We also additionally suggest that a subset of prioritized molecules could be used for evaluation for tuberculosis due to their additional effect against non-replicating tuberculosis as well as the additional hepato-protection offered by the source of these ingredients

New perspectives for pharmacological chaperoning treatment in methylmalonic aciduria cblB type

Methylmalonic aciduria cblB type (MMA cblB) is caused by the impairment of ATP:cob(I)alamin adenosyltransferase (ATR), the enzyme responsible for the synthesis of adenosylcobalamin (AdoCbl) from cob(I)alamin. No definitive treatment is available for patients with this condition and novel therapeutic strategies are therefore much needed. Recently, we described a proof-of-concept regarding the use of pharmacological chaperones as a treatment. This work describes the effect of two potential pharmacological chaperones - compound V (N-{[(4-chlorophenyl)carbamothioyl]amino}-2-phenylacetamide) and compound VI (4-(4-(4-fluorophenyl)-5-methyl-1H-pyrazol-3-yl)benzene-1,3-diol) - on six ATR mutants, including the most common, p.Arg186Trp. Comprehensive functional analysis identified destabilizing (p.Arg186Gln, p.Arg190Cys, p.Arg190His, p.Arg191Gln and p.Glu193Lys) and oligomerization (p.Arg186Trp and p.Arg191Gln) mutations. In a cellular model overexpressing the destabilizing/oligomerization mutations, compounds V and VI had a positive effect on the stability and activity of all ATR variants. When provided in combination with hydroxocobalamin a more positive effect was obtained than with the compounds alone, even in mutations previously described as B12 non-responsive. In addition, a normal oligomerization profile was recovered after treatment of the p.Arg186Trp mutant with both compounds. These promising results confirm MMA cblB type as a conformational disorder and hence, pharmacological chaperones as a new therapeutic option alone or in combination with hydroxocobalamin for many patients with MMA cblBThis work was supported by Instituto de Salud Carlos III and (grant PI13/01239) plus grants from the Fundación Isabel Gemio and Obra Social de La Caixa to BP; the Research Council of Norway [nr. 185181 to AM], The KG Jebsen Foundation, and NovoSeeds (Novo Nordisk). AG was supported by a Ramón y Cajal grant from the Ministerio de Ciencia y Tecnología. This work was supported also by the European Regional Development Fund (PI13/01239

Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters

Assay interference caused by small molecules continues to pose a significant challenge for early drug discovery. A number of rule-based and similarity-based approaches have been derived that allow the flagging of potentially “badly behaving compounds”, “bad actors”, or “nuisance compounds”. These compounds are typically aggregators, reactive compounds, and/or pan-assay interference compounds (PAINS), and many of them are frequent hitters. Hit Dexter is a recently introduced machine learning approach that predicts frequent hitters independent of the underlying physicochemical mechanisms (including also the binding of compounds based on “privileged scaffolds” to multiple binding sites). Here we report on the development of a second generation of machine learning models which now covers both primary screening assays and confirmatory dose–response assays. Protein sequence clustering was newly introduced to minimize the overrepresentation of structurally and functionally related proteins. The models correctly classified compounds of large independent test sets as (highly) promiscuous or nonpromiscuous with Matthews correlation coefficient (MCC) values of up to 0.64 and area under the receiver operating characteristic curve (AUC) values of up to 0.96. The models were also utilized to characterize sets of compounds with specific biological and physicochemical properties, such as dark chemical matter, aggregators, compounds from a high-throughput screening library, drug-like compounds, approved drugs, potential PAINS, and natural products. Among the most interesting outcomes is that the new Hit Dexter models predict the presence of large fractions of (highly) promiscuous compounds among approved drugs. Importantly, predictions of the individual Hit Dexter models are generally in good agreement and consistent with those of Badapple, an established statistical model for the prediction of frequent hitters. The new Hit Dexter 2.0 web service, available at http://hitdexter2.zbh.uni-hamburg.de, not only provides user-friendly access to all machine learning models presented in this work but also to similarity-based methods for the prediction of aggregators and dark chemical matter as well as a comprehensive collection of available rule sets for flagging frequent hitters and compounds including undesired substructures.acceptedVersio

DOGS: Reaction-Driven de novo Design of Bioactive Compounds

We present a computational method for the reaction-based de novo design of drug-like molecules. The software DOGS (Design of Genuine Structures) features a ligand-based strategy for automated ‘in silico’ assembly of potentially novel bioactive compounds. The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features. We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability, based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles. This enables the software to suggest a synthesis route for each designed compound. Two prospective case studies are presented together with details on the algorithm and its implementation. De novo designed ligand candidates for the human histamine H4 receptor and γ-secretase were synthesized as suggested by the software. The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties

Discovery of Novel, Orally Bioavailable, Antileishmanial Compounds Using Phenotypic Screening

Leishmaniasis is a parasitic infection that afflicts approximately 12 million people worldwide. There are several limitations to the approved drug therapies for leishmaniasis, including moderate to severe toxicity, growing drug resistance, and the need for extended dosing. Moreover, miltefosine is currently the only orally available drug therapy for this infection. We addressed the pressing need for new therapies by pursuing a two-step phenotypic screen to discover novel, potent, and orally bioavailable antileishmanials. First, we conducted a high-throughput screen (HTS) of roughly 600,000 small molecules for growth inhibition against the promastigote form of the parasite life cycle using the nucleic acid binding dye SYBR Green I. This screen identified approximately 2,700 compounds that inhibited growth by over 65% at a single point concentration of 10 μM. We next used this 2700 compound focused library to identify compounds that were highly potent against the disease-causing intra-macrophage amastigote form and exhibited limited toxicity toward the host macrophages. This two-step screening strategy uncovered nine unique chemical scaffolds within our collection, including two previously described antileishmanials. We further profiled two of the novel compounds for in vitro absorption, distribution, metabolism, excretion, and in vivo pharmacokinetics. Both compounds proved orally bioavailable, affording plasma exposures above the half-maximal effective concentration (EC50) concentration for at least 12 hours. Both compounds were efficacious when administered orally in a murine model of cutaneous leishmaniasis. One of the two compounds exerted potent activity against trypanosomes, which are kinetoplastid parasites related to Leishmania species. Therefore, this compound could help control multiple parasitic diseases. The promising pharmacokinetic profile and significant in vivo efficacy observed from our HTS hits highlight the utility of our two-step phenotypic screening strategy and strongly suggest that medicinal chemistry optimization of these newly identified scaffolds will lead to promising candidates for an orally available anti-parasitic drug

Shaping a screening file for maximal lead discovery efficiency and effectiveness: elimination of molecular redundancy

High Throughput Screening (HTS) is a successful strategy for finding hits and leads that have the opportunity to be converted into drugs. In this paper we highlight novel computational methods used to select compounds to build a new screening file at Pfizer and the analytical methods we used to assess their quality. We also introduce the novel concept of molecular redundancy to help decide on the density of compounds required in any region of chemical space in order to be confident of running successful HTS campaigns

Phenotypic screening in C. elegansas as a tool for the discovery of new geroprotective drugs

Population aging is one of the largest challenges of the 21st century. As more people live to advanced ages, the prevalence of age-related diseases and disabilities will increase placing an ever larger burden on our healthcare system. A potential solution to this conundrum is to develop treatments that prevent, delay or reduce the severity of age-related diseases by decreasing the rate of the aging process. This ambition has been accomplished in model organisms through dietary, genetic and pharmacological interventions. The pharmacological approaches hold the greatest opportunity for successful translation to the clinic. The discovery of such pharmacological interventions in aging requires high-throughput screening strategies. However, the majority of screens performed for geroprotective drugs inC. elegansso far are rather low throughput. Therefore, the development of high-throughput screening strategies is of utmost importance