Most small molecule drugs interact with unintended, often unknown, biological targets and these off-target interactions may lead to both preclinical and clinical toxic events. Undesired off-target interactions are often not detected using current drug discovery assays, such as experimental polypharmacological screens. Thus, improvement in the early identification of off-target interactions represents an opportunity to reduce safety-related attrition rates during preclinical and clinical development. In order to better identify potential off-target interactions that could be linked to predictable safety issues, a novel computational approach to predict safety-relevant interactions currently not covered was designed and evaluated. These analyses, termed Off-Target Safety Assessment (OTSA), cover more than 7,000 targets (~35% of the proteome) and > 2,46,704 preclinical and clinical alerts (as of January 20, 2019). The approach described herein exploits a highly curated training set of >1 million compounds (tracking >20 million compound-structure activity relationship/SAR data points) with known in vitro activities derived from patents, journals, and publicly available databases. This computational process was used to predict both the primary and secondary pharmacological activities for a selection of 857 diverse small molecule drugs for which extensive secondary pharmacology data are readily available (456 discontinued and 401 FDA approved). The OTSA process predicted a total of 7,990 interactions for these 857 molecules. Of these, 3,923 and 4,067 possible high-scoring interactions were predicted for the discontinued and approved drugs, respectively, translating to an average of 9.3 interactions per drug. The OTSA process correctly identified the known pharmacological targets for >70% of these drugs, but also predicted a significant number of off-targets that may provide additional insight into observed in vivo effects. About 51.5% (2,025) and 22% (900) of these predicted high-scoring interactions have not previously been reported for the discontinued and approved drugs, respectively, and these may have a potential for repurposing efforts. Moreover, for both drug categories, higher promiscuity was observed for compounds with a MW range of 300 to 500, TPSA of ~200, and clogP ≥7. This computation also revealed significantly lower promiscuity (i.e., number of confirmed off-targets) for compounds with MW > 700 and MW<200 for both categories. In addition, 15 internal small molecules with known off-target interactions were evaluated. For these compounds, the OTSA framework not only captured about 56.8% of in vitro confirmed off-target interactions, but also identified the right pharmacological targets for 14 compounds as one of the top scoring targets. In conclusion, the OTSA process demonstrates good predictive performance characteristics and represents an additional tool with utility during the lead optimization stage of the drug discovery process. Additionally, the computed physiochemical properties such as clogP (i.e., lipophilicity), molecular weight, pKa and logS (i.e., solubility) were found to be statistically different between the approved and discontinued drugs, but the internal compounds were close to the approved drugs space in most part.

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Novel Computational Approach to Predict Off-Target Interactions for Small Molecules

The spread of novel virus SARS-CoV-2, well known as COVID-19 has become a major health issue currently which has turned up to a pandemic worldwide. The treatment recommendations are variable. Lack of appropriate medication has worsened the disease. On the basis of prior research, scientists are testing drugs based on medical therapies for SARS and MERS. Many drugs which include lopinavir, ritonavir and thalidomide are listed in the new recommendations. A topological index is a type of molecular descriptor that simply defines numerical values associated with the molecular structure of a compound that is effectively used in modeling many physicochemical properties in numerous quantitative structure-property/activity relationship (QSPR/QSAR) studies. In this study, several degree-based and neighborhood degree sum-based topological indices for several antiviral drugs were investigated by using a M-polynomial and neighborhood M-polynomial methods. In addition, a QSPR was established between the various topological indices and various physicochemical properties of these antiviral drugs along with remdesivir, chloroquine, hydroxychloroquine and theaflavin was performed in order to assess the efficacy of the calculated topological indices. The obtained results reveal that topological indices under study have strong correlation with the physicochemical characteristics of the potential antiviral drugs. A biological activity (pIC50) of these compounds were also investigated by using multiple linear regressions (MLR) analysis.

Topological indices and QSPR/QSAR analysis of some antiviral drugs being investigated for the treatment of COVID-19 patients.

Enoyl-ACP reductases participate in fatty acid biosynthesis by utilizing NADH to reduce the trans double bond between positions C2 and C3 of a fatty acyl chain linked to the acyl carrier protein. The enoyl-ACP reductase from Mycobacterium tuberculosis, known as InhA, is a member of an unusual FAS-II system that prefers longer chain fatty acyl substrates for the purpose of synthesizing mycolic acids, a major component of mycobacterial cell walls. The crystal structure of InhA in complex with NAD+ and a C16 fatty acyl substrate, trans-2-hexadecenoyl-(N-acetylcysteamine)-thioester, reveals that the substrate binds in a general "U-shaped" conformation, with the trans double bond positioned directly adjacent to the nicotinamide ring of NAD+. The side chain of Tyr158 directly interacts with the thioester carbonyl oxygen of the C16 fatty acyl substrate and therefore could help stabilize the enolate intermediate, proposed to form during substrate catalysis. Hydrophobic residues, primarily from the substrate binding loop (residues 196-219), engulf the fatty acyl chain portion of the substrate. The substrate binding loop of InhA is longer than that of other enoyl-ACP reductases and creates a deeper substrate binding crevice, consistent with the ability of InhA to recognize longer chain fatty acyl substrates.

Crystal structure of the Mycobacterium tuberculosis enoyl-ACP reductase, InhA, in complex with NAD+ and a C16 fatty acyl substrate

Pharmaceutical discovery and development is a long and expensive process that, unfortunately, still results in a low success rate, with drug safety continuing to be a major impedance Improved safety screening strategies and methods are needed to more effectively fill this critical gap Recent advances in informatics are now making it possible to manage bigger data sets and integrate multiple sources of screening data in a manner that can potentially improve the selection of higher-quality drug candidates Integrated screening paradigms have become the norm in Pharma, both in discovery screening and in the identification of off-target toxicity mechanisms during later-stage development Furthermore, advances in computational methods are making in silico screens more relevant and suggest that they may represent a feasible option for augmenting the current screening paradigm This paper outlines several fundamental methods of the current drug screening processes across Pharma and emerging techniques/technologies that promise to improve molecule selection In addition, the authors discuss integrated screening strategies and provide examples of advanced screening paradigms

https://journals.sagepub.com/doi/pdf/10.1177/2472555218799713

Screening Strategies and Methods for Better Off-Target Liability Prediction and Identification of Small-Molecule Pharmaceuticals:

<jats:p>The design of the quantitative structure-property/activity relationships for drug-related compounds using theoretical methods relies on appropriate molecular structure representations. The molecular structure of a compound comprises all the information required to determine its chemical, biological, and physical properties. These properties can be assessed by employing a graph theoretical descriptor tool widely known as topological indices. Generalization of descriptors may reduce not only the number of molecular graph-based descriptors but also improve existing results and provide a better correlation to several molecular properties. Recently introduced ve-degree and ev-degree topological indices have been successfully employed for development of models for the prediction of various biological activities/properties. In this article, we propose the general ve-inverse sum indeg index <jats:inline-formula>\n                     <math xmlns=\"http://www.w3.org/1998/Math/MathML\" id=\"M1\">\n                        <msubsup>\n                           <mrow>\n                              <mtext>ISI</mtext>\n                           </mrow>\n                           <mrow>\n                              <mfenced open=\"(\" close=\")\" separators=\"|\">\n                                 <mrow>\n                                    <mi>α</mi>\n                                    <mo>,</mo>\n                                    <mi>β</mi>\n                                 </mrow>\n                              </mfenced>\n                           </mrow>\n                           <mrow>\n                              <mtext>ve</mtext>\n                           </mrow>\n                        </msubsup>\n                        <mfenced open=\"(\" close=\")\" separators=\"|\">\n                           <mrow>\n                              <mi>G</mi>\n                           </mrow>\n                        </mfenced>\n                     </math>\n                  </jats:inline-formula> and general ve-Zagreb index <jats:inline-formula>\n                     <math xmlns=\"http://www.w3.org/1998/Math/MathML\" id=\"M2\">\n                        <msubsup>\n                           <mi>M</mi>\n                           <mi>α</mi>\n                           <mrow>\n                              <mtext>ve</mtext>\n                           </mrow>\n                        </msubsup>\n                        <mfenced open=\"(\" close=\")\" separators=\"|\">\n                           <mrow>\n                              <mi>G</mi>\n                           </mrow>\n                        </mfenced>\n                     </math>\n                  </jats:inline-formula> of graph <jats:inline-formula>\n                     <math xmlns=\"http://www.w3.org/1998/Math/MathML\" id=\"M3\">\n                        <mi>G</mi>\n                     </math>\n                  </jats:inline-formula> and compute <jats:inline-formula>\n                     <math xmlns=\"http://www.w3.org/1998/Math/MathML\" id=\"M4\">\n                        <msubsup>\n                           <mrow>\n                              <mtext>ISI</mtext>\n                           </mrow>\n                           <mrow>\n                              <mfenced open=\"(\" close=\")\" separators=\"|\">\n                                 <mrow>\n                                    <mi>α</mi>\n                                    <mo>,</mo>\n                                    <mi>β</mi>\n                                 </mrow>\n                              </mfenced>\n                           </mrow>\n                           <mrow>\n                              <mtext>ve</mtext>\n                           </mrow>\n                        </msubsup>\n                        <mfenced open=\"(\" close=\")\" separators=\"|\">\n                           <mrow>\n                              <mi>G</mi>\n                           </mrow>\n                        </mfenced>\n                        <mo>,</mo>\n                        <msubsup>\n                           <mi>M</mi>\n                           <mi>α</mi>\n                           <mrow>\n                              <mtext>ve</mtext>\n                           </mrow>\n                        </msubsup>\n                        <mfenced open=\"(\" close=\")\" separators=\"|\">\n                           <mrow>\n                              <mi>G</mi>\n                           </mrow>\n                        </mfenced>\n                     </math>\n                  </jats:inline-formula>, and <jats:inline-formula>\n                     <math xmlns=\"http://www.w3.org/1998/Math/MathML\" id=\"M5\">\n                        <msubsup>\n                           <mi>M</mi>\n                           <mi>α</mi>\n                           <mrow>\n                              <mtext>ev</mtext>\n                           </mrow>\n                        </msubsup>\n                        <mfenced open=\"(\" close=\")\" separators=\"|\">\n                           <mrow>\n                              <mi>G</mi>\n                           </mrow>\n                        </mfenced>\n                     </math>\n                  </jats:inline-formula> (general ev-degree index) of hyaluronic acid-curcumin/paclitaxel conjugates, renowned for its potential anti-inflammatory, antioxidant, and anticancer properties, by using molecular structure analysis and edge partitioning technique. Several ve-degree- and ev-degree-based topological indices are obtained as a special case of <jats:inline-formula>\n                     <math xmlns=\"http://www.w3.org/1998/Math/MathML\" id=\"M6\">\n                        <msubsup>\n                           <mrow>\n                              <mtext>ISI</mtext>\n                           </mrow>\n                           <mrow>\n                              <mfenced open=\"(\" close=\")\" separators=\"|\">\n                                 <mrow>\n                                    <mi>α</mi>\n                                    <mo>,</mo>\n                                    <mi>β</mi>\n                                 </mrow>\n                              </mfenced>\n                           </mrow>\n                           <mrow>\n                              <mtext>ve</mtext>\n                           </mrow>\n                        </msubsup>\n                        <mfenced open=\"(\" close=\")\" separators=\"|\">\n                           <mrow>\n                              <mi>G</mi>\n                           </mrow>\n                        </mfenced>\n                        <mo>,</mo>\n                        <msubsup>\n                           <mi>M</mi>\n                           <mi>α</mi>\n                           <mrow>\n                              <mtext>ve</mtext>\n                           </mrow>\n                        </msubsup>\n                        <mfenced open=\"(\" close=\")\" separators=\"|\">\n                           <mrow>\n                              <mi>G</mi>\n                           </mrow>\n                        </mfenced>\n                     </math>\n                  </jats:inline-formula>, and <jats:inline-formula>\n                     <math xmlns=\"http://www.w3.org/1998/Math/MathML\" id=\"M7\">\n                        <msubsup>\n                           <mi>M</mi>\n                           <mi>α</mi>\n                           <mrow>\n                              <mtext>ev</mtext>\n                           </mrow>\n                        </msubsup>\n                        <mfenced open=\"(\" close=\")\" separators=\"|\">\n                           <mrow>\n                              <mi>G</mi>\n                           </mrow>\n                        </mfenced>\n                     </math>\n                  </jats:inline-formula>. Furthermore, QSPR analysis of <jats:inline-formula>\n                     <math xmlns=\"http://www.w3.org/1998/Math/MathML\" id=\"M8\">\n                        <msubsup>\n                           <mrow>\n                              <mtext>ISI</mtext>\n                           </mrow>\n                           <mrow>\n                              <mfenced open=\"(\" close=\")\" separators=\"|\">\n                                 <mrow>\n                                    <mi>α</mi>\n                                    <mo>,</mo>\n                                    <mi>β</mi>\n                                 </mrow>\n                              </mfenced>\n                           </mrow>\n                           <mrow>\n                              <mtext>ve</mtext>\n                           </mrow>\n                        </msubsup>\n                        <mfenced open=\"(\" close=\")\" separators=\"|\">\n                           <mrow>\n                              <mi>G</mi>\n                           </mrow>\n                        </mfenced>\n                        <mo>,</mo>\n                        <msubsup>\n                           <mi>M</mi>\n                           <mi>α</mi>\n                           <mrow>\n                              <mtext>ve</mtext>\n                           </mrow>\n                        </msubsup>\n                        <mfenced open=\"(\" close=\")\" separators=\"|\">\n                           <mrow>\n                              <mi>G</mi>\n                           </mrow>\n                        </mfenced>\n                     </math>\n                  </jats:inline-formula>, and <jats:inline-formula>\n                     <math xmlns=\"http://www.w3.org/1998/Math/MathML\" id=\"M9\">\n                        <msubsup>\n                           <mi>M</mi>\n                           <mi>α</mi>\n                           <mrow>\n                              <mtext>ev</mtext>\n                           </mrow>\n                        </msubsup>\n                        <mfenced open=\"(\" close=\")\" separators=\"|\">\n                           <mrow>\n                              <mi>G</mi>\n                           </mrow>\n                        </mfenced>\n                     </math>\n                  <

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On Ve-Degree and Ev-Degree Topological Properties of Hyaluronic Acid‐Anticancer Drug Conjugates with QSPR

Small molecular anticancer drugs bind reversibly to DNA and are used in chemotherapy. The experimental measurements of the inhibition activity of drugs are difficult, expensive, and time-consuming. So, quantitative structure–activity/property relationship method (QSAR) is adopted where the dependency of drug binding affinity with their structural features is exploited. To explore whether molecular descriptors can explain their DNA-binding affinity and toxicity, 23 antitumor molecules, which are being used clinically as drugs, are analyzed by rigorous statistical calculations. The 50% cancer cell growth inhibition constant (IC50) for any particular cancer cell line is obtained from the NCI/NIH database. Molecular descriptors (geometrical, physicochemical, and quantum chemical) are calculated for the molecules. A mathematical model is built to predict DNA-drug-binding constant and growth inhibitory concentration by multiple regression which can be useful for rational drug design.

Quantitative Structure–Activity Relationship (QSAR) Study of Some DNA-Intercalating Anticancer Drugs

Meprins are complex and highly glycosylated multi-domain enzymes that require post-translational modifications to reach full activity. Meprins are metalloproteases of the astacin family characterized by a conserved zinc-binding motif (HExxHxxGFxHExxRxDR). Human meprin-α and -β protease subunits are 55% identical at the amino acid level, however the substrate and peptide bond specificities vary markedly. Current work focuses on the critical amino acid residues in the non-primed subsites of human meprins-α and -β involved in inhibitor/ligand binding. To compare the molecular events underlying ligand affinity, homology modeling of the protease domain of humep-α and -β based on the astacin crystal structure followed by energy minimization and molecular dynamics simulation of fully solvated proteases with inhibitor Pro-Leu-Gly-hydroxamate in S subsites were performed. The solvent accessible surface area curve shows a decrease in solvent accessibility values at specific residues upon inhibitor binding. The potential energy, total energy, H-bond interactions, root mean square deviation and root mean square fluctuation plot reflect the subtle differences in the S subsite of the enzymes which interact with different residues at P site of the inhibitor.

Comparative analysis of binding sites of human meprins with hydroxamic acid derivative by molecular dynamics simulation study

With the emergence of multidrug resistant mycobacterium tuberculosis, it is an immediate necessity to design new potent drugs. One step toward it is to understand the mechanism of inhibition of the enzyme InhA (trans-2-enoyl-ACP-reductase), which has an important function in the building of micobacterial cell wall. An attempt through molecular modeling and simulation methods is presented here to explain binding affinities of different derivatives of drugs and also the role of some important amino acid residues in the active site of the InhA.

Computational Methodologies Followed in Structure Based In-Silico Drug Design: An Example

Human meprin-α and-β are important regulators of angiogenesis, cancer, inflammation, fibrosis, and neurodegenerative diseases and hence important therapeutic targets. Meprins are the only astacin proteases that are expressed in membrane-bound and secreted form. The cleavage specificity of human meprins is similar in certain cases but differs markedly in others. The inhibitor selectivity of human meprins is controlled by the specific residues involved in binding at the active-site cleft of the proteases. Meprins are inhibited by various small molecular inhibitors as well as macromolecular endogenous inhibitors, making them good drug targets. In the current study, molecular dynamics simulation was performed for 10 ns on ten systems consisting of two apoenzymes of meprin -α/β and eight complexes of human meprin-α and -β complexed to four inhibitors with different metal binding moieties and comparable Ki values. These simulation studies helped to elucidate the molecular details of how several parameters influence protein-inhibitor binding affinity. Analysis of the interaction energies of the protein-inhibitor complexes revealed the diverse binding nature of this series of inhibitors. Several structural segments of human meprins exhibited certain conformational changes during the simulation time course. Among the inhibitors studied captopril had a different disposition in the meprin-bound complexes compared to the other three inhibitors, namely Pro- Leu-Gly-hydroxamate, galardin and EDTA. Comparison of the interaction energies for each system helped us to conclude that the hydroxamic acid-based inhibitors are the most potent inhibitors of meprins.

Insights from Analysis of Binding Sites of Human Meprins: Screening of Inhibitors by Molecular Dynamics Simulation Study.

In the present work, a ligand-based 3D pharmacophore and QSAR approach is used for the selection of potentially active compounds for inhibitory action against the enoyl-ACP-reductase (InhA) from Mycobacterium tuberculosis, followed by molecular modelling, dynamic simulation and binding energy calculation methods. The biological activity of the molecules is measured by logIC50 (50% inhibitory concentration). The molecular descriptors are used to build statistical models to predict the biological activity of interest.

Indrani Sarkar

Papers

Quantitative Structure–Activity Relationship (QSAR) Study of Some DNA-Intercalating Anticancer Drugs

Comparative analysis of binding sites of human meprins with hydroxamic acid derivative by molecular dynamics simulation study

Computational Methodologies Followed in Structure Based In-Silico Drug Design: An Example

Insights from Analysis of Binding Sites of Human Meprins: Screening of Inhibitors by Molecular Dynamics Simulation Study.

To Explore Compounds as Tuberculosis Inhibitors—A Combination of Pharmacophore Modelling, Virtual Screening and Molecular Docking Studies