Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics

Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies

Globally, malignancies cause one out of six mortalities, which is a serious health problem. Cancer therapy has always been challenging, apart from major advances in immunotherapies, stem cell transplantation, targeted therapies, hormonal therapies, precision medicine, and palliative care, and traditional therapies such as surgery, radiation therapy, and chemotherapy. Natural products are integral to the development of innovative anticancer drugs in cancer research, offering the scientific community the possibility of exploring novel natural compounds against cancers. The role of natural products like Vincristine and Vinblastine has been thoroughly implicated in the management of leukemia and Hodgkin's disease. The computational method is the initial key approach in drug discovery, among various approaches. This review investigates the synergy between natural products and computational techniques, and highlights their significance in the drug discovery process. The transition from computational to experimental validation has been highlighted through in vitro and in vivo studies, with examples such as betulinic acid and withaferin A. The path toward therapeutic applications have been demonstrated through clinical studies of compounds such as silvestrol and artemisinin, from preclinical investigations to clinical trials. This article also addresses the challenges and limitations in the development of natural products as potential anti-cancer drugs. Moreover, the integration of deep learning and artificial intelligence with traditional computational drug discovery methods may be useful for enhancing the anticancer potential of natural products.

Large-scale computational screening of Indian medicinal plants reveals Cassia angustifolia to be a potentially anti-diabetic

Researchers are investigating the medicinal properties of herbal plants throughout the world, which often leads to the discovery of novel plants and their chemicals for prophylactic needs of humans. Natural phytochemicals continue to be sought as alternative treatments for various diseases because of their non-toxic and therapeutic properties. In recent years, computational phytochemistry has enabled large-scale screening of phytochemicals, enabling researchers to pursue a wide range of therapeutic research alternatives to traditional ethnopharmacology. We propose to identify an anti-diabetic plant by computational screening on Indian herbal plants in conjunction with experimental characterization and biological validation. The methodology involves the creation of an in-house Indian herbal plant database. Molecular docking is used to screen against alpha amylase for anti-diabetic prophylaxis. Cassia angustifolia was chosen because its phytochemicals are able to bind to alpha amylase. Plants were experimentally extracted, botanically studied and their biological activity was evaluated. Further, the use of molecular dynamics was then applied to pinpoint the phytochemicals responsible for the affinity of alpha amylase. Results in the phytochemical analysis of the extracts revealed strong presence of alkaloids, flavonoids and cardiac glycosides. Moreover, alpha amylase biological activity with C. angustifolia extracts of chloroform, hexane and ethyl acetate demonstrated activity of 3.26, 8.01 and 30.33 µg/ml validating computational predictions. In conclusion, this study developed, validated computational predictions of identifying potential anti-diabetic plants 'Cassia angustifolia' from house herbal databases. Hope this study shall inspire explore plant therapeutic repurposing using computational methods of drug discovery.Communicated by Ramaswamy H. Sarma.

Fluoroquinolone Analogs, SAR Analysis, and the Antimicrobial Evaluation of 7-Benzimidazol-1-yl-fluoroquinolone in In Vitro, In Silico, and In Vivo Models

Structure-activity relationship (SAR) studies allow the evaluation of the relationship between structural chemical changes and biological activity. Fluoroquinolones have chemical characteristics that allow their structure to be modified and new analogs with different therapeutic properties to be generated. The objective of this research is to identify and select the C-7 heterocycle fluoroquinolone analog (FQH 1-5) with antibacterial activity similar to the reference fluoroquinolone through in vitro, in silico, and in vivo evaluations. First, SAR analysis was conducted on the FQH 1-5, using an in vitro antimicrobial sensibility model in order to select the best compound. Then, an in silico model mechanism of action analysis was carried out by molecular docking. The non-bacterial cell cytotoxicity was evaluated, and finally, the antimicrobial potential was determined by an in vivo model of topical infection in mice. The results showed antimicrobial differences between the FQH 1-5 and Gram-positive and Gram-negative bacteria, identifying the 7-benzimidazol-1-yl-fluoroquinolone (FQH-2) as the most active against S. aureus. Suggesting the same mechanism of action as the other fluoroquinolones; no cytotoxic effects on non-bacterial cells were found. FQH-2 was demonstrated to decrease the amount of bacteria in infected wound tissue.

Carboxymuconolactone decarboxylase is a prospective molecular target for multi-drug resistant Acinetobacter baumannii-computational modeling, molecular docking and dynamic simulation studies

Multidrug-resistant Acinetobacter baumannii (MDRAb), a priority-I pathogen declared by the World Health Organization, became a potential healthcare concern worldwide with a high mortality rate. Thus, the identification of putative molecular targets and potential lead molecules is an important concern in healthcare. The present study aimed to screen a prospective molecular target and effectual binders for the drug discovery of MDRAb by computational virtual screening approach. Based on the functional role, γ-carboxymuconolactone decarboxylase (CMD) was prioritized as the target and its three-dimensional (3D) structure was computationally modeled. Based on the availability of the 3D structure, twenty-five herbal molecules were selected by database search, and their drug-likeliness, pharmacokinetic, and toxicity features were predicted. The effectual binding of the selected molecules towards CMD was predicted by molecular docking. The stability of the best-docked complexes was predicted by molecular dynamics (MD) simulation for 100 ns and binding energy calculations were carried out by molecular mechanics generalized Born and surface area solvation (MM/GBSA) method. Out of twenty-five molecules screened, hirsutine (an indole alkaloid of Uncaria rhynchophylla) and thymoquinone (a phytochemical of Nigella sativa) were qualified for drug likeliness, pharmacokinetic, and toxicity features and demonstrated significant effectual binding to CMD when compared with the binding of co-crystallized inhibitor and CMD (control). The docked complexes of hirsutine and thymoquinone, and CMD were stabilized by the binding energies of -8. 30 and -8. 46 kcal/mol respectively. These molecules were qualified in terms of ideal drug likeliness, ADME, and toxicity properties. MD simulation studies showed that the ligand-protein complexes were stable throughout the simulation. The binding free energies of the complexes by MMGBSA were estimated to be -42.08157745 kcal/mol and -36.58618242 kcal/mol for hirsutine and thymoquinone respectively when compared with the calculated binding free energy of the control (-28.75032666 kcal/mol). This study concluded that hirsutine and thymoquinone can act as potential lead molecules against CMD and the present hypothesis can be scaled up to develop potential inhibitors against MDRAb.

In Silico Screening and Molecular Dynamics Simulation Studies in the Identification of Natural Compound Inhibitors Targeting the Human Norovirus RdRp Protein to Fight Gastroenteritis

Norovirus (HNoV) is a leading cause of gastroenteritis globally, and there are currently no treatment options or vaccines available to combat it. RNA-dependent RNA polymerase (RdRp), one of the viral proteins that direct viral replication, is a feasible target for therapeutic development. Despite the discovery of a small number of HNoV RdRp inhibitors, the majority of them have been found to possess a little effect on viral replication, owing to low cell penetrability and drug-likeness. Therefore, antiviral agents that target RdRp are in high demand. For this purpose, we used in silico screening of a library of 473 natural compounds targeting the RdRp active site. The top two compounds, ZINC66112069 and ZINC69481850, were chosen based on their binding energy (BE), physicochemical and drug-likeness properties, and molecular interactions. ZINC66112069 and ZINC69481850 interacted with key residues of RdRp with BEs of -9.7, and -9.4 kcal/mol, respectively, while the positive control had a BE of -9.0 kcal/mol with RdRp. In addition, hits interacted with key residues of RdRp and shared several residues with the PPNDS, the positive control. Furthermore, the docked complexes showed good stability during the molecular dynamic simulation of 100 ns. ZINC66112069 and ZINC69481850 could be proven as potential inhibitors of the HNoV RdRp in future antiviral medication development investigations.

Antiplasmodial activity of coumarins isolated from Polygala boliviensis: in vitro and in silico studies

Polygala boliviensis is found in the Brazilian semiarid region. This specie is little chemically and biologically studied. Polygala spp. have different metabolites, especially coumarins. Studies indicate that coumarins have antimalarial potential, denoting the importance of researching new active compounds from plants, since the resistance of Plasmodium strains to conventional therapy has increased. The present study aimed to evaluate the antiplasmodial activity of auraptene and poligalen against a chloroquine-resistant strain of Plasmodium falciparum. Coumarins were isolated from P. boliviensis by open column chromatography and identified by Nuclear Magnetic Resonance Spectroscopy. A cytotoxicity assay was carried out using MTT test, and the in vitro antiplasmodial activity was evaluated using the W2 strain. The antiplasmodial activity results found were IC50=0.171 ± 0.016 for auraptene and 0.164 ± 0.012 for poligalen; the selectivity indexes were 78.71 and 609.76, respectively. Inverse virtual screening in the BRAMMT database by OCTOPUS 1.2 was applied to coumarins to find potential P. falciparum targets and showed higher affinity energy of auraptene for purine nucleoside phosphorylase (PfPNP) and of poligalen for dihydroorotate dehydrogenase (PfDHODH). Molecular Dynamics studies (MD and MM-GBSA) approach were applied to calculate binding energies against selected P. falciparum targets and showed that all coumarins were stable at the binding site during simulations. Furthermore, energies were favorable for complexation. This is the first report of auraptene in P. boliviensis species and of in vitro antiplasmodial activity of auraptene and poligalen. In silico studies indicated that the mechanism of action of coumarins is the inhibition of PfPNP and PfDHODH.Communicated by Ramaswamy H. Sarma.

Computational Evaluation of Peptide-Protein Binding Affinities: Application of Potential of Mean Force Calculations to SH2 Domains

Many biological functions are mediated by protein-protein interactions (PPIs), often involving specific structural modules, such as SH2 domains. Inhibition of PPIs is a pharmaceutical strategy of growing importance. However, a major challenge in the design of PPI inhibitors is the large interface involved in these interactions, which, in many cases, makes inhibition by small organic molecules ineffective. Peptides, which cover a wide range of dimensions and can be opportunely designed to mimic protein sequences at PPI interfaces, represent a valuable alternative to small molecules. Computational techniques able to predict the binding affinity of peptides for the target domain or protein represent a crucial stage in the workflow for the design of peptide-based drugs. This chapter describes a protocol to obtain the potential of mean force (PMF) for peptide-SH2 domain binding, starting from umbrella sampling (US) molecular dynamics (MD) simulations. The PMF profiles can be effectively used to predict the relative standard binding free energies of different peptide sequences.