Application of computational methods for anticancer drug discovery, design, and optimization

Comprehensive review of the repositioning of non-oncologic drugs for cancer immunotherapy

Drug repositioning or repurposing has gained worldwide attention as a plausible way to search for novel molecules for the treatment of particular diseases or disorders. Drug repurposing essentially refers to uncovering approved or failed compounds for use in various diseases. Cancer is a deadly disease and leading cause of mortality. The search for approved non-oncologic drugs for cancer treatment involved in silico modeling, databases, and literature searches. In this review, we provide a concise account of the existing non-oncologic drug molecules and their therapeutic potential in chemotherapy. The mechanisms and modes of action of the repurposed drugs using computational techniques are also highlighted. Furthermore, we discuss potential targets, critical pathways, and highlight in detail the different challenges pertaining to drug repositioning for cancer immunotherapy.

Predicting Plasmodium falciparum kinase inhibitors from antimalarial medicinal herbs using computational modeling approach

Malaria remains a significant public health challenge, with resistance to available drugs necessitating the development of novel therapies targeting invasion-dependent proteins. Plasmodium falciparum calcium-dependent protein kinase 1 (PfCDPK-1) is essential for host erythrocyte invasion and parasite asexual development. This study screened a library of 490 compounds using computational methods to identify potential PfCDPK-1 inhibitors. Three compounds; 17-hydroxyazadiradione, Picracin, and Epicatechin-gallate derived from known antimalarial botanicals, showed potent inhibitory effects on PfCDPK-1. These compounds exhibited better binding affinities (-8.8, -9.1, -9.3 kCal/mol respectively), pharmacokinetics, and physicochemical properties than the purported inhibitory standard of PfCDPK-1, Purfalcamine. Molecular dynamics simulations (50 ns) and molecular mechanics analyses confirmed the stability and binding rigidity of these compounds at the active pocket of PfCDPK-1. The results suggest that these compounds are promising pharmacological targets with potential therapeutic effects for malaria treatment/management without undesirable side effects. Therefore, this study provides new insights into the development of effective antimalarial agents targeting invasion-dependent proteins, which could help combat the global malaria burden.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00175-z.

Structural analysis and ensemble docking revealed the binding modes of selected progesterone receptor modulators

Uterine fibroids (UF) are benign smooth muscle neoplasm of uterus that have a significant impact on a woman's quality of life as they perturb hormonal homeostasis resulting in heavy menstrual bleeding, impaired fertility, pregnancy complications and loss. UF can be surgically removed through invasive procedures, but their recurrence rate is often high. Progesterone receptor (PR) has an imperative role in UF management. Mifepristone, ulipristal acetate (UPA) and asoprisnil (ASO) are some selective progesterone receptor modulators (SPRMs), acts on PR, but due to their side effects in long term use, they were withdrawn from the market. Hence, there is a dire need for novel, highly efficient with least side effects, therapeutics for the treatment of UF. To contribute toward the drug discovery for UF, we made an extensive structural comparison of reported PR crystal structures, also elucidated the binding modes of four existing SPRMs against human PR through ensemble docking approach. Our studies revealed the presence of 5 highly repeating water molecules that has an important role in ligand binding and structural stability. Our ensemble docking and MD simulation revealed that studied ligands have preferential selectivity toward the specific conformation of PR. It is anticipated that our study will be a useful resource to all the drug discovery scientists who are engaged in the identification of lead molecules against UF.

Nanoinformatics and Personalized Medicine: An Advanced Cumulative Approach for Cancer Management

BACKGROUND

Even though the battle against one of the deadliest diseases, cancer, has advanced remarkably in the last few decades and the survival rate has improved significantly; the search for an ultimate cure remains a utopia. Nanoinformatics, which is bioinformatics coupled with nanotechnology, endows many novel research opportunities in the preclinical and clinical development of personalized nanosized drug carriers in cancer therapy. Personalized nanomedicines serve as a promising treatment option for cancer owing to their noninvasiveness and their novel approach. Explicitly, the field of personalized medicine is expected to have an enormous impact soon because of its many advantages, namely its versatility to adapt a drug to a cohort of patients.

OBJECTIVE

The current review explains the application of this newly emerging field called nanoinformatics to the field of precision medicine. This review also recapitulates how nanoinformatics could hasten the development of personalized nanomedicine for cancer, which is undoubtedly the need of the hour.

CONCLUSION

This approach has been facilitated by a humongous impending field named Nanoinformatics. These breakthroughs and advances have provided insight into the future of personalized medicine. Imperatively, they have been enabling landmark research to merge all advances, creating nanosized particles that contain drugs targeting cell surface receptors and other potent molecules designed to kill cancerous cells. Nanoparticle- based medicine has been developing and has become a center of attention in recent years, focusing primely on proficient delivery systems for various chemotherapy drugs.

Toward the Identification of Natural Antiviral Drug Candidates against Merkel Cell Polyomavirus: Computational Drug Design Approaches

Merkel cell carcinoma (MCC) is a rare form of aggressive skin cancer mainly caused by Merkel cell polyomavirus (MCPyV). Most MCC tumors express MCPyV large T (LT) antigens and play an important role in the growth-promoting activities of oncoproteins. Truncated LT promotes tumorigenicity as well as host cell proliferation by activating the viral replication machinery, and inhibition of this protein in humans drastically lowers cellular growth linked to the corresponding cancer. Our study was designed with the aim of identifying small molecular-like natural antiviral candidates that are able to inhibit the proliferation of malignant tumors, especially those that are aggressive, by blocking the activity of viral LT protein. To identify potential compounds against the target protein, a computational drug design including molecular docking, ADME (absorption, distribution, metabolism, and excretion), toxicity, molecular dynamics (MD) simulation, and molecular mechanics generalized Born surface area (MM-GBSA) approaches were applied in this study. Initially, a total of 2190 phytochemicals isolated from 104 medicinal plants were screened using the molecular docking simulation method, resulting in the identification of the top five compounds having the highest binding energy, ranging between −6.5 and −7.6 kcal/mol. The effectiveness and safety of the selected compounds were evaluated based on ADME and toxicity features. A 250 ns MD simulation confirmed the stability of the selected compounds bind to the active site (AS) of the target protein. Additionally, MM-GBSA analysis was used to determine the high values of binding free energy (ΔG bind) of the compounds binding to the target protein. The five compounds identified by computational approaches, Paulownin (CID: 3084131), Actaealactone (CID: 11537736), Epigallocatechin 3-O-cinnamate (CID: 21629801), Cirsilineol (CID: 162464), and Lycoricidine (CID: 73065), can be used in therapy as lead compounds to combat MCPyV-related cancer. However, further wet laboratory investigations are required to evaluate the activity of the drugs against the virus.

Molecular modelling studies and identification of novel phytochemical inhibitor of DLL3

Prostate cancer has been recently considered the most diagnosed cancer in male. DLL3 is overexpressed in CRPC-NE but not in localised prostate cancer or BPH. There are no effective treatments for neuroendocrine differentiated prostate cancer due to a lack of understanding of DLL3 structure and function. The structure of DLL3 is not yet determined using any experimental techniques. Hence, the structure-based drug discovery approach against prostate cancer has not shown great success. In present study, molecular modelling techniques were employed to generate three-dimensional structure of DLL3 and performed its thorough structural analysis. Further, all-atom molecular dynamics simulation was performed to obtain energetically favourable conformation. Further, we used a virtual screening using a library of >13800 phytochemicals from the IMPPAT database and other literature to select the best possible phytochemical inhibitor for DLL3 and identified the top five compounds. Relative binding affinity was calculated using the MM-PBSA approach. ADMET properties of the screened compounds reveal the toxic effect of Gnemonol C. We believe these studied physicochemical properties, functional domain identification, and binding site identification would be very useful to gain more structural and functional insights of DLL3; also, it can be used to infer their pharmacodynamics properties of DLL3 which was recently reported as an important prostate cancer target. The current study also proposes that Ergosterol Peroxide, Dioslupecin A, Mulberrofuran K, and Caracurine V have strong affinities and could serve as plausible inhibitors against DLL3. We believe this study would further help develop better drug candidates against neuroendocrine prostate cancer.Communicated by Ramaswamy H. Sarma.

Update and New Insights on Future Cancer Drug Candidates From Plant-Based Alkaloids

Cancer is a complex multifactorial disease that results from alterations in many physiological and biochemical functions. Over the last few decades, it has become clear that cancer cells can acquire multidrug resistance to conventional anticancer drugs, resulting in tumor relapse. Thus, there is a continuous need to discover new and effective anticancer drugs. Natural products from plants have served as a primary source of cancer drugs and continue to provide new plant-derived anticancer drugs. The present review describes plant-based alkaloids, which have been reported as active or potentially active in cancer treatment within the past 4 years (2017-2020), both in preclinical research and/or in clinical trials. In addition, recent insights into the possible molecular mechanism of action of alkaloid prodrugs naturally present in plants are also highlighted.

The Challenging Melanoma Landscape: From Early Drug Discovery to Clinical Approval

Melanoma is recognized as the most dangerous type of skin cancer, with high mortality and resistance to currently used treatments. To overcome the limitations of the available therapeutic options, the discovery and development of new, more effective, and safer therapies is required. In this review, the different research steps involved in the process of antimelanoma drug evaluation and selection are explored, including information regarding in silico, in vitro, and in vivo experiments, as well as clinical trial phases. Details are given about the most used cell lines and assays to perform both two- and three-dimensional in vitro screening of drug candidates towards melanoma. For in vivo studies, murine models are, undoubtedly, the most widely used for assessing the therapeutic potential of new compounds and to study the underlying mechanisms of action. Here, the main melanoma murine models are described as well as other animal species. A section is dedicated to ongoing clinical studies, demonstrating the wide interest and successful efforts devoted to melanoma therapy, in particular at advanced stages of the disease, and a final section includes some considerations regarding approval for marketing by regulatory agencies. Overall, considerable commitment is being directed to the continuous development of optimized experimental models, important for the understanding of melanoma biology and for the evaluation and validation of novel therapeutic strategies.

Computational Studies on Acetylcholinesterase Inhibitors: From Biochemistry to Chemistry

Acetylcholinesterase inhibitors are the most promising therapeutics for Alzheimer's disease treatment as these prevent the loss of acetylcholine and slows the progression of the disease. The drugs approved for the management of Alzheimer's disease by the FDA are acetylcholinesterase inhibitors but are associated with side effects. Consistent and stringent efforts by the researchers with the help of computational methods opened new ways of developing novel molecules with good acetylcholinesterase inhibitory activity. In this manuscript, we reviewed the studies that identified the essential structural features of acetylcholinesterase inhibitors at the molecular level as well as the techniques like molecular docking, molecular dynamics, quantitative structure-activity relationship, virtual screening, and pharmacophore modelling that were used in designing these inhibitors.

Cheminformatic Characterization of Natural Antimicrobial Products for the Development of New Lead Compounds

The growing antimicrobial resistance (AMR) of pathogenic organisms to currently prescribed drugs has resulted in the failure to treat various infections caused by these superbugs. Therefore, to keep pace with the increasing drug resistance, there is a pressing need for novel antimicrobial agents, especially from non-conventional sources. Several natural products (NPs) have been shown to display promising in vitro activities against multidrug-resistant pathogens. Still, only a few of these compounds have been studied as prospective drug candidates. This may be due to the expensive and time-consuming process of conducting important studies on these compounds. The present review focuses on applying cheminformatics strategies to characterize, prioritize, and optimize NPs to develop new lead compounds against antimicrobial resistance pathogens. Moreover, case studies where these strategies have been used to identify potential drug candidates, including a few selected open-access tools commonly used for these studies, are briefly outlined.

Rational Design of Novel Inhibitors of α-Glucosidase: An Application of Quantitative Structure Activity Relationship and Structure-Based Virtual Screening

α-Glucosidase is considered a prime drug target for Diabetes Mellitus and its inhibitors are used to delay carbohydrate digestion for the treatment of diabetes mellitus. With the aim to design α-glucosidase inhibitors with novel chemical scaffolds, three folds ligand and structure based virtual screening was applied. Initially linear quantitative structure activity relationship (QSAR) model was developed by a molecular operating environment (MOE) using a training set of thirty-two known inhibitors, which showed good correlation coefficient (r² = 0.88), low root mean square error (RMSE = 0.23), and cross-validated correlation coefficient r² (q² = 0.71 and RMSE = 0.31). The model was validated by predicting the biological activities of the test set which depicted r² value of 0.82, indicating the robustness of the model. For virtual screening, compounds were retrieved from zinc is not commercial (ZINC) database and screened by molecular docking. The best docked compounds were chosen to assess their pharmacokinetic behavior. Later, the α-glucosidase inhibitory potential of the selected compounds was predicted by their mode of binding interactions. The predicted pharmacokinetic profile, docking scores and protein-ligand interactions revealed that eight compounds preferentially target the catalytic site of α-glucosidase thus exhibit potential α-glucosidase inhibition in silico. The α-glucosidase inhibitory activities of those Hits were predicted by QSAR model, which reflect good inhibitory activities of these compounds. These results serve as a guidelines for the rational drug design and development of potential novel anti-diabetic agents.

Developing Novel Anticancer Drugs for Targeted Populations: An Update

BACKGROUND

Due to higher failure rates, lengthy time and high cost of the traditional de novo drug discovery and development process, the rate of opportunity to get new, safe and efficacious drugs for the targeted population, including pediatric patients with cancer, becomes sluggish.

OBJECTIVES

This paper discusses the development of novel anticancer drugs focusing on the identification and selection of targeted anticancer drug development for the targeted population.

METHODS

Information presented in this review was obtained from different databases, including PUBMED, SCOPUS, Web of Science, and EMBASE. Various keywords were used as search terms.

RESULTS

The pharmaceutical companies currently are executing drug repurposing as an alternative means to accelerate the drug development process that reduces the risk of failure, time and cost, which take 3-12 years with almost 25% overall probability of success as compared to de novo drug discovery and development process (10- 17 years) which has less than 10% probability of success. An alternative strategy to the traditional de novo drug discovery and development process, called drug repurposing, is also presented.

CONCLUSION

Therefore, to continue with the progress of developing novel anticancer drugs for the targeted population, identification and selection of target to specific disease type is important. Considering the aspects of the age of the patient and the disease stages such as each cancer types are different when we study the disease at a molecular level. Drug repurposing technique becomes an influential alternative strategy to discover and develop novel anticancer drug candidates.

A Pipeline to Call Multilevel Expression Changes between Cancer and Normal Tissues and Its Applications in Repurposing Drugs Effective for Gastric Cancer

Differential gene analyses on gastric cancer usually focus on expression change of single genes between tumor and adjacent normal tissues. However, besides changes on single genes, there are also coexpression and expression network module changes during the development of gastric cancer. In this study, we proposed a pipeline to investigate various levels of changes between gastric cancer and adjacent normal tissues, which were used to repurpose potential drugs for treating gastric cancer. Specifically, we performed a series of analyses on 242 gastric cancer samples (33-normal, 209-cancer) downloaded from the cancer genome atlas (TCGA), including data quality control, differential gene analysis, gene coexpression network analysis, module function enrichment analysis, differential coexpression analysis, differential pathway analysis, and screening of potential therapeutic drugs. In the end, we discovered some genes and pathways that are significantly different between cancer and adjacent normal tissues (such as the interleukin-4 and interleukin-13 signaling pathway) and screened perturbed genes by 2703 drugs that have a high overlap with the identified differentially expressed genes. Our pipeline might be useful for understanding cancer pathogenesis as well as gastric cancer treatment.

A newly isolated strain of Halomonas sp. (HA1) exerts anticancer potential via induction of apoptosis and G2/M arrest in hepatocellular carcinoma (HepG2) cell line

Marine bacterial strains are of great interest for their ability to produce secondary metabolites with anticancer potentials. Isolation, identification, characterization and anticancer activities of isolated bacteria from El-Hamra Lake, Wadi El-Natrun (Egypt) were the objectives of this study. The isolated bacteria were identified as a moderately halophilic alkaliphilic strain. Ethyl acetate extraction was performed and identified by liquid chromatography-mass spectrophotometry (LC–MS–MS) and nuclear magnetic resonance analysis (NMR). Cytotoxicity of the extract was assessed on the HepG2 cell line and normal human peripheral lymphocytes (HPBL) in vitro. Halomonas sp. HA1 extract analyses revealed anticancer potential. Many compounds have been identified including cyclo-(Leu-Leu), cyclo-(Pro-Phe), C17-sphinganine, hexanedioic acid, bis (2-ethylhexyl) ester, surfactin C14 and C15. The extract exhibited an IC₅₀ of 68 ± 1.8 μg/mL and caused marked morphological changes in treated HepG2 cells. For mechanistic anticancer evaluation, 20 and 40 µg/mL of bacterial extract were examined. The up-regulation of apoptosis-related genes' expression, P53, CASP-3, and BAX/BCL-2 at mRNA and protein levels proved the involvement of P53-dependant mitochondrial apoptotic pathway. The anti-proliferative properties were confirmed by significant G₂/M cell cycle arrest and PCNA down-regulation in the treated cells. Low cytotoxicity was observed in HPBL compared to HepG2 cells. In conclusion, results suggest that the apoptotic and anti-proliferative effects of Halomonas sp. HA1 extract on HepG2 cells can provide it as a candidate for future pharmaceutical industries.

Discovering Anti-Cancer Drugs via Computational Methods

New drug discovery has been acknowledged as a complicated, expensive, time-consuming, and challenging project. It has been estimated that around 12 years and 2.7 billion USD, on average, are demanded for a new drug discovery via traditional drug development pipeline. How to reduce the research cost and speed up the development process of new drug discovery has become a challenging, urgent question for the pharmaceutical industry. Computer-aided drug discovery (CADD) has emerged as a powerful, and promising technology for faster, cheaper, and more effective drug design. Recently, the rapid growth of computational tools for drug discovery, including anticancer therapies, has exhibited a significant and outstanding impact on anticancer drug design, and has also provided fruitful insights into the area of cancer therapy. In this work, we discussed the different subareas of the computer-aided drug discovery process with a focus on anticancer drugs.

Exploring structural features of EGFR-HER2 dual inhibitors as anti-cancer agents using G-QSAR approach

Simultaneous inhibition of EGFR and HER2 by dual-targeting inhibitors is an established anti-cancer strategy. Therefore, a recent trend in drug discovery involves understanding the features of such dual inhibitors. In this study, three different G-QSAR models were developed corresponding to individual EGFR, HER2 and the dual-model for both receptors. The dual-model provided site-specific information wherein (i) increasing electronegative character and higher index of saturated carbon at R4 position; (ii) presence of chlorine atom at R2 position; (iii) decreasing alpha modified shape index at R1 and R3 positions; and (iv) less electronegativity at R2 position; were found important for enhancing the dual activity. Also, comparison of dual-model with the EGFR/HER2 individual models revealed that it incorporates the properties of both models and, thus, represents a combination of EGFR/HER2. Further, fragment analysis revealed that R2 and R4 are important for imparting high potency while specificity is decided by R1/R3 fragment. We also checked the predictive ability of the dual-model by determining applicability domain using William's plot. Also, analysis of active molecules showed they show favorable substitutions that agree with the constructed dual-model. Thus, we have been successful in developing a single dual-response QSAR model to get an insight into various structural features influencing EGFR/HER2 activity.

Interaction of Selected Terpenoids From Dalbergia sissoo With Catalytic Domain of Matrix Metalloproteinase-1: An In Silico Assessment of Their Anti-wrinkling Potential

Matrix metalloproteinase-1 (MMP-1) is a predominant collagenase enzyme that cleaves collagen fibers, contributing to skin wrinkling. Matrix metalloproteinase-1 inhibitors of herbal origin may provide an earnest probability to offer a novel curative approach against MMP-1-mediated collagenolysis, prompted by ultraviolet (UV)-induced overexpression of MMP-1. In this in silico study, we have explored the MMP-1 inhibitory potential of selected terpenoids from Dalbergia sissoo extracts. Two triterpenoids (lupeol and betulin), 1 diterpenoid (phytol), and 1 ester derivative of lupeol (lupeol acetate) were studied along with a reference inhibitor (doxycycline) using molecular docking approach. Non covalent interaction between the target ligands was found. Lupeol was found interacting with amino acid (AA) residues in the catalytic domain of MMP-1 with 3 hydrogen bonds (H-bond) formation, phytol with 1 and doxycycline with 2 H-bonds, whereas betulin and lupeol acetate were not able to form any H-bond with the AA residues in the catalytic site of the target protein. However, hydrophobic interaction between these ligands and protein was evident with select residues. The binding affinity of lupeol was highest (binding free energy, ΔG = −8.24 kcal/mol), which was greater than reference drug, doxycycline (ΔG = −8.05 kcal/mol). Lupeol acetate and phytol displayed a ΔG value of −7.12 and −7.06 kcal/mol, respectively, whereas betulin holds less binding affinity for the target receptor (ΔG = −4.66 kcal/mol). In silico pharmacokinetic studies demonstrated drug-like properties of the ligand compounds. This study shows that hydroxyl groups present in the ligands play a substantial role in establishing protein ligand interaction via hydrogen bonding.

Synthesis, In Vitro Evaluation, Molecular Docking and DFT Studies of Some Phenyl Isothiocyanates as Anticancer Agents

BACKGROUND

Isothiocyanates (ITCs) are small molecules that are important in synthetic organic chemistry, but their actual importance lies in their potential as anti-carcinogens. Through this piece of work, an effort was made to assess the anti-cancer activity of some simple ITCs which can be synthesized through easy greener pathways.

METHODS

Cell proliferation assay was performed on ovarian cancer cells (PA-1) and non-tumorigenic ovarian epithelial cells (IOSE-364). Furthermore, qRT-PCR for transcript expression levels of Spindlin1 and caspases in ovarian cancer cells and cell cycle analysis was performed. In silico studies were incorporated to understand the mode of ligand-protein interaction, ADME/Toxicity and drug-likeliness parameters. Density functional theory studies have been also been employed on the ITCs to assess their efficiency in anticancer activity.

RESULTS

An inexpensive, environmentally benign pathway has been developed for synthesizing a series of ITCs. Among the synthesized ITCs, NC6 showed better cytotoxic effects as compared to its counterparts. Novel findings revealed that NC6 had 5-folds lower transcript expression levels of Spindlin1 and induced caspases 3 and 7 expressions assessed by qRT-PCR in ovarian cancer cells. Furthermore, flow cytometry assay showed the cell cycle arrest at G1/S phase of cell cycle. The molecular docking studies revealed favorable binding affinities and the physiochemical parameters were predicted to be compatible with drug-likeliness.

CONCLUSION

The results demonstrated the possibility that small isothiocyanate molecules which can be synthesized by a simple green methodology, can pose as promising candidates for their application as anticancer agents.

From biomedicinal to in silico models and back to therapeutics: a review on the advancement of peptidic modeling

Bioactive peptides participate in numerous metabolic functions of living organisms and have emerged as potential therapeutics on a diverse range of diseases. Albeit peptide design does not go without challenges, overwhelming advancements on in silico methodologies have increased the scope of peptide-based drug design and discovery to an unprecedented amount. Within an in silico model versus an experimental validation scenario, this review aims to summarize and discuss how different in silico techniques contribute at present to the design of peptide-based molecules. Published in silico results from 2014 to 2018 were selected and discriminated in major methodological groups, allowing a transversal analysis, promoting a landscape vision and asserting its increasing value in drug design.

A Structure-Based Drug Discovery Paradigm

Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the “big data” generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery.

Therapeutic value of steroidal alkaloids in cancer: Current trends and future perspectives

Discovery and development of new potentially selective anticancer agents are necessary to prevent a global cancer health crisis. Currently, alternative medicinal agents derived from plants have been extensively investigated to develop anticancer drugs with fewer adverse effects. Among them, steroidal alkaloids are conventional secondary metabolites that comprise an important class of natural products found in plants, marine organisms and invertebrates, and constitute a judicious choice as potential anti-cancer leads. Traditional medicine and modern science have shown that representatives from this compound group possess potential antimicrobial, analgesic, anticancer and anti-inflammatory effects. Therefore, systematic and recapitulated information about the bioactivity of these compounds, with special emphasis on the molecular or cellular mechanisms, is of high interest. In this review, we methodically discuss the in vitro and in vivo potential of the anticancer activity of natural steroidal alkaloids and their synthetic and semi-synthetic derivatives. This review focuses on cumulative and comprehensive molecular mechanisms, which will help researchers understand the molecular pathways involving steroid alkaloids to generate a selective and safe new lead compound with improved therapeutic applications for cancer prevention and therapy. In vitro and in vivo studies provide evidence about the promising therapeutic potential of steroidal alkaloids in various cancer cell lines, but advanced pharmacokinetic and clinical experiments are required to develop more selective and safe drugs for cancer treatment.

Molecular dynamics and combined docking studies for the identification of Zaire ebola virus inhibitors

Ebola virus (EBOV) is a lethal human pathogen with a risk of global spread of its zoonotic infections, and Ebolavirus Zaire specifically has the highest fatality rate amongst other species. There is a need for continuous effort towards having therapies, as a single licensed treatment to neutralize the EBOV is yet to come into reality. This present study virtually screened the MCULE database containing almost 36 million compounds against the structure of a Zaire Ebola viral protein (VP) 35 and a consensus scoring of both MCULE and CLCDDW docking programs remarked five compounds as potential hits. These compounds, with binding energies ranging from -7.9 to -8.9 kcal/mol, were assessed for predictions of their physicochemical and bioactivity properties, as well as absorption, distribution, metabolism, excretion, and toxicity (ADMET) criteria. The results of the 50 ns molecular dynamics simulations showed the presence of dynamic stability between ligand and protein complexes, and the structures remained significantly unchanged at the ligand-binding site throughout the simulation period. Both docking analysis and molecular dynamics simulation studies suggested strong binding affinity towards the receptor cavity and these selected compounds as potential inhibitors against the Zaire Ebola VP 35. With respect to inhibition constant values, bioavailability radar and other physicochemical properties, compound A (MCULE-1018045960-0-1) appeared to be the most promising hit compound. However, the ligand efficiency and ligand efficiency scale need improvement during optimization, and also validation via in vitro and in vivo studies are necessary to finally make a lead compound in treating Ebola virus diseases. Communicated by Ramaswamy H. Sarma.

Target-based drug discovery through inversion of quantitative structure-drug-property relationships and molecular simulation: CA IX-sulphonamide complexes

In this work, a target-based drug screening method is proposed exploiting the synergy effect of ligand-based and structure-based computer-assisted drug design. The new method provides great flexibility in drug design and drug candidates with considerably lower risk in an efficient manner. As a model system, 45 sulphonamides (33 training, 12 testing ligands) in complex with carbonic anhydrase IX were used for development of quantitative structure-activity-lipophilicity (property)-relationships (QSPRs). For each ligand, nearly 5,000 molecular descriptors were calculated, while lipophilicity (logk_w) and inhibitory activity (logK_i) were used as drug properties. Genetic algorithm-partial least squares (GA-PLS) provided a QSPR model with high prediction capability employing only seven molecular descriptors. As a proof-of-concept, optimal drug structure was obtained by inverting the model with respect to reference drug properties. 3509 ligands were ranked accordingly. Top 10 ligands were further validated through molecular docking. Large-scale MD simulations were performed to test the stability of structures of selected ligands obtained through docking complemented with biophysical experiments.

Unraveling the bioactivity of anticancer peptides as deduced from machine learning

Cancer imposes a global health burden as it represents one of the leading causes of morbidity and mortality while also giving rise to significant economic burden owing to the associated expenditures for its monitoring and treatment. In spite of advancements in cancer therapy, the low success rate and recurrence of tumor has necessitated the ongoing search for new therapeutic agents. Aside from drugs based on small molecules and protein-based biopharmaceuticals, there has been an intense effort geared towards the development of peptide-based therapeutics owing to its favorable and intrinsic properties of being relatively small, highly selective, potent, safe and low in production costs. In spite of these advantages, there are several inherent weaknesses that are in need of attention in the design and development of therapeutic peptides. An abundance of data on bioactive and therapeutic peptides have been accumulated over the years and the burgeoning area of artificial intelligence has set the stage for the lucrative utilization of machine learning to make sense of these large and high-dimensional data. This review summarizes the current state-of-the-art on the application of machine learning for studying the bioactivity of anticancer peptides along with future outlook of the field. Data and R codes used in the analysis herein are available on GitHub at https://github.com/Shoombuatong2527/anticancer-peptides-review.

Ethnomedicine Claim Directed in Silico Prediction of Anticancer Activity

BACKGROUND

The merits of ethnomedicine-led approach to identify and prioritize anticancer medicinal plants have been challenged as cancer is more likely to be poorly understood in traditional medicine practices. Nonetheless, it is also believed that useful data can be generated by combining ethnobotanical findings with available scientific studies. Thus, this study combined an ethnobtanical study with ligand based in silico screening to identify relevant medical plants and predict their anticancer potential based on their phytoconstiutents reported in scientific literatures.

METHODS

First, relevant medicinal plants were identified through an ethnobotanical survey. A list of phytochemicals was prepared based on literature review of articles which reported on the natural products of identified medicinal plants. Then, their phytochemicals were subjected to in silico evaluation, which included a hybrid score similarity measure, rule of five, Ghose-Viswanadhan-Wendoloski (GVW)-indices and structural features criteria, to predict their anticancer activity and drugability.

RESULTS

A total of 18 medicinal plants and 265 phytoconstituents were identified. The natural product pool constituted 109(41.13%) terpenoids, 67(25.28%) phenolics, 29(10.94%) simple and functionalized hydrocarbons, 26(9.81%) alkaloids, 25(9.43%) glycosides and 9(3.40%) compounds belonging to different phytochemical classes. The similarity measure using CDRUG identified 34(12.73%) phytochemicals with high (p-Value < 0.05) and 35(13.21%) with moderate possibility (p-Value < 0.1) of anticancer activity. In fact, three of the predicted compounds had the same structure with known anticancer compounds (HSCORE=1). The 80% GVW-indices based antineoplastic drugabilityranges were all mate by 25 of the predicted compounds. Predicted compounds were also shown to have ring structures and functional groups deemed important for anticancer activity.

CONCLUSIONS

Given the findings, there is a promising anticancer activity by the traditionally used medicinal plants and a potential for the predicted phytochemicals to be pursued as possible hits or me-too drugs.