Computational strategies to identify, prioritize and design potential antimalarial agents from natural products

Philosophiae Doctor - PhDIntroduction: There is an exigent need to develop novel antimalarial drugs in view of the mounting disease burden and emergent resistance to the presently used drugs against the malarial parasites. A large amount of natural products, especially those used in ethnomedicine for malaria, have shown varying in-vitro antiplasmodial activities. Facilitating antimalarial drug development from this wealth of natural products is an imperative and laudable mission to pursue. However, the limited resources, high cost, low prospect and the high cost of failure during preclinical and clinical studies might militate against pursue of this mission. Chemoinformatics techniques can simulate and predict essential molecular properties required to characterize compounds thus eliminating the cost of equipment and reagents to conduct essential preclinical studies, especially on compounds that may fail during drug development. Therefore, applying chemoinformatics techniques on natural products with in-vitro antiplasmodial activities may facilitate identification and prioritization of these natural products with potential for novel mechanism of action, desirable pharmacokinetics and high likelihood for development into antimalarial drugs. In addition, unique structural features mined from these natural products may be templates to design new potential antimalarial compounds. Method: Four chemoinformatics techniques were applied on a collection of selected natural products with in-vitro antiplasmodial activity (NAA) and currently registered antimalarial drugs (CRAD): molecular property profiling, molecular scaffold analysis, machine learning and design of a virtual compound library. Molecular property profiling included computation of key molecular descriptors, physicochemical properties, molecular similarity analysis, estimation of drug-likeness, in-silico pharmacokinetic profiling and exploration of structure-activity landscape. Analysis of variance was used to assess statistical significant differences in these parameters between NAA and CRAD. Next, molecular scaffold exploration and diversity analyses were performed on three datasets (NAA, CRAD and malarial data from Medicines for Malarial Ventures (MMV)) using scaffold counts and cumulative scaffold frequency plots. Scaffolds from the NAA were compared to those from CRAD and MMV. A Scaffold Tree was also generated for all the datasets. Thirdly, machine learning approaches were used to build four regression and four classifier models from bioactivity data of NAA using molecular descriptors and molecular fingerprints. Models were built and refined by leave-one-out cross-validation and evaluated with an independent test dataset. Applicability domain (AD), which defines the limit of reliable predictability by the models, was estimated from the training dataset and validated with the test dataset. Possible chemical features associated with reported antimalarial activities of the compounds were also extracted. Lastly, virtual compound libraries were generated with the unique molecular scaffolds identified from the NAA. The virtual compounds generated were characterized by evaluating selected molecular descriptors, toxicity profile, structural diversity from CRAD and prediction of antiplasmodial activity. Results: From the molecular property profiling, a total of 1040 natural products were selected and a total of 13 molecular descriptors were analyzed. Significant differences were observed between the natural products with in-vitro antiplasmodial activities (NAA) and currently registered antimalarial drugs (CRAD) for at least 11 of the molecular descriptors. Molecular similarity and chemical space analysis identified NAA that were structurally diverse from CRAD. Over 50% of NAA with desirable drug-like properties were identified. However, nearly 70% of NAA were identified as potentially "promiscuous" compounds. Structure-activity landscape analysis highlighted compound pairs that formed "activity cliffs". In all, prioritization strategies for the natural products with in-vitro antiplasmodial activities were proposed. The scaffold exploration and analysis results revealed that CRAD exhibited greater scaffold diversity, followed by NAA and MMV respectively. Unique scaffolds that were not contained in any other compounds in the CRAD datasets were identified in NAA. The Scaffold Tree showed the preponderance of ring systems in NAA and identified virtual scaffolds, which maybe potential bioactive compounds or elucidate the NAA possible synthetic routes. From the machine learning study, the regression and classifier models that were most suitable for NAA were identified as model tree M5P (correlation coefficient = 0.84) and Sequential Minimization Optimization (accuracy = 73.46%) respectively. The test dataset fitted into the applicability domain (AD) defined by the training dataset. The “amine” group was observed to be essential for antimalarial activity in both NAA and MMV dataset but hydroxyl and carbonyl groups may also be relevant in the NAA dataset. The results of the characterization of the virtual compound library showed significant difference (p value 90%) of the virtual compound library. The virtual compound libraries showed sufficient diversity in structures and majority were structurally diverse from currently registered antimalarial drugs. Finally, up to 70% of the virtual compounds were predicted as active antiplasmodial agents. Conclusions:Molecular property profiling of natural products with in-vitro antiplasmodial activities (NAA) and currently registered antimalarial drugs (CRAD) produced a wealth of information that may guide decisions and facilitate antimalarial drug development from natural products and led to a prioritized list of natural products with in-vitro antiplasmodial activities. Molecular scaffold analysis identified unique scaffolds and virtual scaffolds from NAA that possess desirable drug-like properties, which make them ideal starting points for molecular antimalarial drug design. The machine learning study built, evaluated and identified amply accurate regression and classifier accurate models that were used for virtual screening of natural compound libraries to mine possible antimalarial compounds without the expense of bioactivity assays. Finally, a good amount of the virtual compounds generated were structurally diverse from currently registered antimalarial drugs and potentially active antiplasmodial agents. Filtering and optimization may lead to a collection of virtual compounds with unique chemotypes that may be synthesized and added to screening deck against Plasmodium

A perspective on nanotechnology and covid-19 vaccine research and production in south africa

Advances in nanotechnology have enabled the development of a new generation of vaccines, which are playing a critical role in the global control of the COVID-19 pandemic and the return to normalcy. Vaccine development has been conducted, by and large, by countries in the global north. South Africa, as a major emerging economy, has made extensive investments in nanotechnology and bioinformatics and has the expertise and resources in vaccine development and manufacturing. This has been built at a national level through decades of investment. In this perspective article, we provide a synopsis of the investments made in nanotechnology and highlight how these could support innovation, research, and development for vaccines for this disease. We also discuss the application of bioinformatics tools to support rapid and cost-effective vaccine development and make recommendations for future research and development in this area to support future health challenges

State-of-the-art strategies to prioritize Mycobacterium tuberculosis drug targets for drug discovery using a subtractive genomics approach

Tuberculosis remains one of the causes of death from a single infectious bacterium. The inappropriate use of antibiotics and patients’ non-compliance among other factors drive the emergence of drug-resistant tuberculosis. Multidrug-resistant and extensively drug-resistant strains of tuberculosis pose significant challenges to current treatment regimens, as their reduced efficacy against these strains limits successful patient outcomes. Furthermore, the limited effectiveness and associated toxicity of second-line drugs further compound the issue. Moreover, the scarcity of novel pharmacological targets and the subsequent decline in the number of anti-TB compounds in the drug development pipeline has further hindered the emergence of new therapies. As a result, researchers need to develop innovative approaches to identify potential new anti-TB drugs. The evolution of technology and the breakthrough in omics data allow the use of computational biology approaches, for example, metabolomic analysis to uncover pharmacological targets for structured-based drug design. The role of metabolism in pathogen development, growth, survival, and infection has been established. Therefore, this review focuses on the M. tb metabolic network as a hub for novel target identification and highlights a step-by-step subtractive genomics approach for target prioritization

Exploration of scaffolds from natural products with antiplasmodial activities, currently registered antimalarial drugs and public malarial screen data

In light of current resistance to antimalarial drugs, there is a need to discover new classes of antimalarial agents with unique mechanisms of action. Identification of unique scaffolds from natural products with in vitro antiplasmodial activities may be the starting point for such new classes of antimalarial agents. We therefore conducted scaffold diversity and comparison analysis of natural products with in vitro antiplasmodial activities (NAA), currently registered antimalarial drugs (CRAD) and malaria screen data from Medicine for Malaria Ventures (MMV). The scaffold diversity analyses on the three datasets were performed using scaffold counts and cumulative scaffold frequency plots. Scaffolds from the NAA were compared to those from CRAD and MMV. A Scaffold Tree was also generated for each of the datasets and the scaffold diversity of NAA was found to be higher than that of MMV. Among the NAA compounds, we identified unique scaffolds that were not contained in any of the other compound datasets. These scaffolds from NAA also possess desirable drug-like properties making them ideal starting points for antimalarial drug design considerations. The Scaffold Tree showed the preponderance of ring systems in NAA and identified virtual scaffolds, which may be potential bioactive compounds

Prioritization of anti-malarial hits from nature: Chemo-informatic profiling of natural products with in vitro antiplasmodial activities and currently registered anti-malarial drugs

A large number of natural products have shown in vitro antiplasmodial activities. Early identification and prioritization of these natural products with potential for novel mechanism of action, desirable pharmacokinetics and likelihood for development into drugs is advantageous. Chemo-informatic profiling of these natural products were conducted and compared to currently registered anti-malarial drugs (CRAD). Natural products with in vitro antiplasmodial activities (NAA) were compiled from various sources. These natural products were sub-divided into four groups based on inhibitory concentration (IC50). Key molecular descriptors and physicochemical properties were computed for these compounds and analysis of variance used to assess statistical significance amongst the sets of compounds. Molecular similarity analysis, estimation of drug-likeness, in silico pharmacokinetic profiling, and exploration of structure–activity landscape were also carried out on these sets of compounds

Cheminformatic Characterization of Natural Antimicrob al Products for the Development of New Lead Compounds

The growing antimicrobial resistance (AMR) of pathogenic organisms to currently pre- scribed 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

Cheminformatic Profiling and Hit Prioritization of Natural Products with Activities against Methicillin-Resistant Staphylococcus aureus (MRSA)

Several natural products (NPs) have displayed varying in vitro activities against methicillin-resistant Staphylococcus aureus (MRSA). However, few of these compounds have not been developed into potential antimicrobial drug candidates. This may be due to the high cost and tedious and time-consuming process of conducting the necessary preclinical tests on these compounds. In this study, cheminformatic profiling was performed on 111 anti-MRSA NPs (AMNPs), using a few orally administered conventional drugs for MRSA (CDs) as reference, to identify compounds with prospects to become drug candidates. This was followed by prioritizing these hits and identifying the liabilities among the AMNPs for possible optimization. Cheminformatic profiling revealed that most of the AMNPs were within the required drug-like region of the investigated properties. For example, more than 76% of the AMNPs showed compliance with the Lipinski, Veber, and Egan predictive rules for oral absorption and permeability. About 34% of the AMNPs showed the prospect to penetrate the blood–brain barrier (BBB), an advantage over the CDs, which are generally non-permeant of BBB. The analysis of toxicity revealed that 59% of the AMNPs might have negligible or no toxicity risks. Structure–activity relationship (SAR) analysis revealed chemical groups that may be determinants of the reported bioactivity of the compounds. A hit prioritization strategy using a novel “desirability scoring function” was able to identify AMNPs with the desired drug-likeness. Hit optimization strategies implemented on AMNPs with poor desirability scores led to the design of two compounds with improved desirability scores

Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach

In view of the vast number of natural products with potential antiplasmodial bioactivity and cost of conducting antiplasmodial bioactivity assays, it may be judicious to learn from previous antiplasmodial bioassays and predict bioactivity of these natural products before experimental bioassays. This study set out to harness antimalarial bioactivity data of natural products to build accurate predictive models, utilizing classical machine learning approaches, which can find potential antimalarial hits from new sets of natural products. Classical machine learning approaches were used to build four classifier models (Naïve Bayesian, Voted Perceptron, Random Forest and Sequence Minimization Optimization of Support Vector Machines) from bioactivity data of natural products with in-vitro antiplasmodial activity (NAA) using a combination of the molecular descriptors and two-dimensional molecular fingerprints of the compounds. Models were evaluated with an independent test dataset. Possible chemical features associated with reported antimalarial activities of the compounds were also extracted. From the results, Random Forest (accuracy 82.81%, Kappa statistics 0.65 and Area under Receiver Operating Characteristics curve 0.91) and Sequential Minimization Optimization (accuracy 85.93%, Kappa statistics 0.72 and Area under Receiver Operating Characteristics curve 0.86) showed good predictive performance for the NAA dataset. The amine chemical group (specifically alkyl amines and basic nitrogen) was confirmed to be essential for antimalarial activity in active NAA dataset. This study built and evaluated classifier models that were used to predict the antiplasmodial bioactivity class (active or inactive) of a set of natural products from interBioScreen chemical library

Computational applications in secondary metabolite discovery (caismd): An online workshop

We report the major conclusions of the online open-access workshop “Computational Applications in Secondary Metabolite Discovery (CAiSMD)” that took place from 08 to 10 March 2021. Invited speakers from academia and industry and about 200 registered participants from fve continents (Africa, Asia, Europe, South America, and North America) took part in the workshop. The workshop highlighted the potential applications of computational meth‑ odologies in the search for secondary metabolites (SMs) or natural products (NPs) as potential drugs and drug leads. During 3 days, the participants of this online workshop received an overview of modern computer-based approaches for exploring NP discovery in the “omics” age. The invited experts gave keynote lectures, trained participants in handson sessions, and held round table discussions. This was followed by oral presentations with much interaction between the speakers and the audience. Selected applicants (early-career scientists) were ofered the opportunity to give oral presentations (15 min) and present posters in the form of fash presentations (5 min) upon submission of an abstract. The fnal program available on the workshop website (https://caismd.indiayouth.info/) comprised of 4 keynote lec‑ tures (KLs), 12 oral presentations (OPs), 2 round table discussions (RTDs), and 5 hands-on sessions (HSs). This meeting report also references internet resources for computational biology in the area of secondary metabolites that are of use outside of the workshop areas and will constitute a long-term valuable source for the community. The workshop concluded with an online survey form to be completed by speakers and participants for the goal of improving any subsequent editions

Computational drug repurposing strategy predicted peptide-based drugs that can potentially inhibit the interaction of SARS-CoV-2 spike protein with its target (humanACE2).

Drug repurposing for COVID-19 has several potential benefits including shorter development time, reduced costs and regulatory support for faster time to market for treatment that can alleviate the current pandemic. The current study used molecular docking, molecular dynamics and protein-protein interaction simulations to predict drugs from the Drug Bank that can bind to the SARS-CoV-2 spike protein interacting surface on the human angiotensin-converting enzyme 2 (hACE2) receptor. The study predicted a number of peptide-based drugs, including Sar9 Met (O2)11-Substance P and BV2, that might bind sufficiently to the hACE2 receptor to modulate the protein-protein interaction required for infection by the SARS-CoV-2 virus. Such drugs could be validated in vitro or in vivo as potential inhibitors of the interaction of SARS-CoV-2 spike protein with the human angiotensin-converting enzyme 2 (hACE2) in the airway. Exploration of the proposed and current pharmacological indications of the peptide drugs predicted as potential inhibitors of the interaction between the spike protein and hACE2 receptor revealed that some of the predicted peptide drugs have been investigated for the treatment of acute respiratory distress syndrome (ARDS), viral infection, inflammation and angioedema, and to stimulate the immune system, and potentiate antiviral agents against influenza virus. Furthermore, these predicted drug hits may be used as a basis to design new peptide or peptidomimetic drugs with better affinity and specificity for the hACE2 receptor that may prevent interaction between SARS-CoV-2 spike protein and hACE2 that is prerequisite to the infection by the SARS-CoV-2 virus