Computational polypharmacology comes of age

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Identification of Promising Drug Candidates against Prostate Cancer through Computationally-Driven Drug Repurposing

Prostate cancer (PC) is one of the most common types of cancer in males. Although early stages of PC are generally associated with favorable outcomes, advanced phases of the disease present a significantly poorer prognosis. Moreover, currently available therapeutic options for the treatment of PC are still limited, being mainly focused on androgen deprivation therapies and being characterized by low efficacy in patients. As a consequence, there is a pressing need to identify alternative and more effective therapeutics. In this study, we performed large-scale 2D and 3D similarity analyses between compounds reported in the DrugBank database and ChEMBL molecules with reported anti-proliferative activity on various PC cell lines. The analyses included also the identification of biological targets of ligands with potent activity on PC cells, as well as investigations on the activity annotations and clinical data associated with the more relevant compounds emerging from the ligand-based similarity results. The results led to the prioritization of a set of drugs and/or clinically tested candidates potentially useful in drug repurposing against PC

Chemoinformatics Analyses of Tau Ligands Reveal Key Molecular Requirements for the Identification of Potential Drug Candidates against Tauopathies

Tau is a highly soluble protein mainly localized at a cytoplasmic level in the neuronal cells, which plays a crucial role in the regulation of microtubule dynamic stability. Recent studies have demonstrated that several factors, such as hyperphosphorylation or alterations of Tau metabolism, may contribute to the pathological accumulation of protein aggregates, which can result in neuronal death and the onset of a number of neurological disorders called Tauopathies. At present, there are no available therapeutic remedies able to reduce Tau aggregation, nor are there any structural clues or guidelines for the rational identification of compounds preventing the accumulation of protein aggregates. To help identify the structural properties required for anti-Tau aggregation activity, we performed extensive chemoinformatics analyses on a dataset of Tau ligands reported in ChEMBL. The performed analyses allowed us to identify a set of molecular properties that are in common between known active ligands. Moreover, extensive analyses of the fragment composition of reported ligands led to the identification of chemical moieties and fragment combinations prevalent in the more active compounds. Interestingly, many of these fragments were arranged in recurring frameworks, some of which were clearly present in compounds currently under clinical investigation. This work represents the first in-depth chemoinformatics study of the molecular properties, constituting fragments and similarity profiles, of known Tau aggregation inhibitors. The datasets of compounds employed for the analyses, the identified molecular fragments and their combinations are made publicly available as supplementary material

Prediction of activity and selectivity profiles of human Carbonic Anhydrase inhibitors using machine learning classification models

The development of selective inhibitors of the clinically relevant human Carbonic Anhydrase (hCA) isoforms IX and XII has become a major topic in drug research, due to their deregulation in several types of cancer. Indeed, the selective inhibition of these two isoforms, especially with respect to the homeostatic isoform II, holds great promise to develop anticancer drugs with limited side effects. Therefore, the development of in silico models able to predict the activity and selectivity against the desired isoform(s) is of central interest. In this work, we have developed a series of machine learning classification models, trained on high confidence data extracted from ChEMBL, able to predict the activity and selectivity profiles of ligands for human Carbonic Anhydrase isoforms II, IX and XII. The training datasets were built with a procedure that made use of flexible bioactivity thresholds to obtain well-balanced active and inactive classes. We used multiple algorithms and sampling sizes to finally select activity models able to classify active or inactive molecules with excellent performances. Remarkably, the results herein reported turned out to be better than those obtained by models built with the classic approach of selecting an a priori activity threshold. The sequential application of such validated models enables virtual screening to be performed in a fast and more reliable way to predict the activity and selectivity profiles against the investigated isoforms

Selection of protein conformations for structure-based polypharmacology studies

Several drugs exert their therapeutic effect through the modulation of multiple targets. Structure-based approaches hold great promise for identifying compounds with the desired polypharmacological profiles. These methods use knowledge of the protein binding sites to identify stereoelectronically complementary ligands. The selection of the most suitable protein conformations to be used in the design process is vital, especially for multitarget drug design in which the same ligand has to be accommodated in multiple binding pockets. Herein, we focus on currently available techniques for the selection of the most suitable protein conformations for multitarget drug design, compare the potential advantages and limitations of each method, and comment on how their combination could help in polypharmacology drug design

Drug Repurposing and Polypharmacology to Fight SARS-CoV-2 Through Inhibition of the Main Protease

The outbreak of a new coronavirus (SARS-CoV-2), which is responsible for the COVID-19 disease and is spreading rapidly around the world, urgently requires effective therapeutic treatments. In this context, drug repurposing represents a valuable strategy, as it enables accelerating the identification of drug candidates with already known safety profiles, possibly aiding in the late stages of clinical evaluation. Moreover, therapeutic treatments based on drugs with beneficial multi-target activities (polypharmacology) may show an increased antiviral activity or help to counteract severe complications concurrently affecting COVID-19 patients. In this study, we present the results of a computational drug repurposing campaign that aimed at identifying potential inhibitors of the main protease (Mpro) of the SARS-CoV-2. The performed in silico screening allowed the identification of 22 candidates with putative SARS-CoV-2 Mpro inhibitory activity. Interestingly, some of the identified compounds have recently entered clinical trials for COVID-19 treatment, albeit not being assayed for their SARS-CoV-2 antiviral activity. Some candidates present a polypharmacology profile that may be beneficial for COVID-19 treatment and, to the best of our knowledge, have never been considered in clinical trials. For each repurposed compound, its therapeutic relevance and potential beneficial polypharmacological effects that may arise due to its original therapeutic indication are thoroughly discussed

Hydroxamic Acid Derivatives: From Synthetic Strategies to Medicinal Chemistry Applications

Since the approval of three hydroxamic acid-based HDAC inhibitors as anticancer drugs, such functional groups acquired even more notoriety in synthetic medicinal chemistry. The ability of hydroxamic acids (HAs) to chelate metal ions makes this moiety an attractive metal binding group-in particular, Fe(III) and Zn(II)-so that HA derivatives find wide applications as metalloenzymes inhibitors. In this minireview, we will discuss the most relevant features concerning hydroxamic acid derivatives. In a first instance, the physicochemical characteristics of HAs will be summarized; then, an exhaustive description of the most relevant methods for the introduction of such moiety into organic substrates and an overview of their uses in medicinal chemistry will be presented

Structure-activity exploration of a small-molecule allosteric inhibitor of T790M/L858R double mutant EGFR

EGFR is a protein kinase whose aberrant activity is frequently involved in the development of non-small lung cancer (NSCLC) drug resistant forms. The allosteric inhibition of this enzyme is currently one among the most attractive approaches to design and develop anticancer drugs. In a previous study, we reported the identification of a hit compound acting as type III allosteric inhibitor of the L858R/T790M double mutant EGFR. Herein, we report the design, synthesis and in vitro testing of a series of analogues of the previously identified hit with the aim of exploring the structure-activity relationships (SAR) around this scaffold. The performed analyses allowed us to identify two compounds 15 and 18 showing improved inhibition of double mutant EGFR with respect to the original hit, as well as interesting antiproliferative activity against H1975 NSCLC cancer cells expressing double mutant EGFR. The newly discovered compounds represent promising starting points for further hit-to-lead optimisation

On the integration of in silico drug design methods for drug repurposing

Drug repurposing has become an important branch of drug discovery. Several computational approaches that help to uncover new repurposing opportunities and aid the discovery process have been put forward, or adapted from previous applications. A number of successful examples are now available. Overall, future developments will greatly benefit from integration of different methods, approaches and disciplines. Steps forward in this direction are expected to help to clarify, and therefore to rationally predict, new drug-target, target-disease, and ultimately drug-disease associations