Progress in Pharmacometrics Implementation and Regulatory Integration in Africa: A Systematic Review.

The availability of clinical trial data, advocacy, and increased funding has facilitated the implementation of pharmacometrics in Africa, resulting in the establishment of additional training programs for pharmacometricians. This study conducted a systematic review to evaluate the progress made from the implementation of pharmacometrics in clinical drug development and its adoption into drug approval by regulatory authorities in Africa. We performed a comprehensive literature search using major databases such as PubMed and Google Scholar. The study included articles published until 2024, with no lower cutoff. Articles were excluded if not addressing the research question or of pharmacometrics studies done outside Africa with no collaboration with African researchers (study setting). For the review, a total of 121 articles were included for analysis. Among the reported pharmacometrics approaches, Population pharmacokinetics modeling approaches are the most used (95 (78.5%)). South Africa and Uganda researchers have the most research output in pharmacometrics in Africa (82 (89.1%) and 7 (7.61%), respectively), with the University of Cape Town (South Africa) producing the highest (71 (78.8%)) of all article in Africa. The most studied conditions are TB (43 (35.5%)), HIV (33 (27.3%), TB and HIV (22 (18.2%)), and malaria (12 (9.92%). Pharmacometrics is gaining momentum in Africa, and the progress made since inception will significantly improve the safety and efficacy of therapeutic agents used to treat HIV, TB, and other emerging conditions.

Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors.

Artificial Intelligence is transforming drug discovery, particularly in the hit identification phase of therapeutic compounds. One tool that has been instrumental in this transformation is Quantitative Structure-Activity Relationship (QSAR) analysis. This computer-aided drug design tool uses machine learning to predict the biological activity of new compounds based on the numerical representation of chemical structures against various biological targets. With diabetes mellitus becoming a significant health challenge in recent times, there is intense research interest in modulating antidiabetic drug targets. α-Glucosidase is an antidiabetic target that has gained attention due to its ability to suppress postprandial hyperglycaemia, a key contributor to diabetic complications. This review explored a detailed approach to developing QSAR models, focusing on strategies for generating input variables (molecular descriptors) and computational approaches ranging from classical machine learning algorithms to modern deep learning algorithms. We also highlighted studies that have used these approaches to develop predictive models for α-glucosidase inhibitors to modulate this critical antidiabetic drug target.

HDAC3 inhibitors: a patent review of their broad-spectrum applications as therapeutic agents.

INTRODUCTION: Histone deacetylases (HDACs) are a class of zinc-dependent enzymes. They maintain acetylation homeostasis, with numerous biological functions and are associated with many diseases. HDAC3 strictly requires multi-subunit complex formation for activity. It is associated with the progression of numerous non-communicable diseases. Its widespread involvement in diseases makes it an epigenetic drug target. Preexisting HDAC3 inhibitors have many uses, highlighting the need for continued research in the discovery of HDAC3-selective inhibitors. AREA COVERED: This review provides an overview of 24 patents published from 2010 to 2023, focusing on compounds that inhibit the HDAC3 isoenzyme. EXPERT OPINION: HDAC3-selective inhibitors - pivotal for pharmacological applications, as single or combination therapies - are gaining traction as a strategy to move away from complications laden pan-HDAC inhibitors. Moreover, there is an unmet need for HDAC3 inhibitors with alternative zinc-binding groups (ZBGs) because some preexisting ZBGs have limitations related to toxicity and side effects. Difficulties in achieving HDAC3 selectivity may be due to isoform selectivity. However, advancements in computer-aided drug design and experimental data of HDAC3 3D co-crystallized models could lead to the discovery of novel HDAC3-selective inhibitors, which bear alternative ZBGs with balanced selectivity for HDAC3 and potency.

The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials.

Antimicrobial resistance (AMR) has become more of a concern in recent decades, particularly in infections associated with global public health threats. The development of new antibiotics is crucial to ensuring infection control and eradicating AMR. Although drug discovery and development are essential processes in the transformation of a drug candidate from the laboratory to the bedside, they are often very complicated, expensive, and time-consuming. The pharmaceutical sector is continuously innovating strategies to reduce research costs and accelerate the development of new drug candidates. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology that renews the hope of researchers for the faster identification, design, and development of cheaper, less resource-intensive, and more efficient drug candidates. In this review, we discuss an overview of AMR, the potential, and limitations of CADD in AMR drug discovery, and case studies of the successful application of this technique in the rapid identification of various drug candidates. This review will aid in achieving a better understanding of available CADD techniques in the discovery of novel drug candidates against resistant pathogens and other infectious agents.

In-silico and in-vitro screening of Asiatic acid and Asiaticoside A against Cathepsin S enzyme.

BACKGROUND: Atherosclerosis is a form of cardiovascular disease that affects the endothelium of the blood vessel. Series of events are involved in the pathophysiology of this disease which includes the breaking down of the connective tissue elastin and collagen responsible for the tensile strength of the arterial wall by proteolytic enzyme. One of these enzymes called Cathepsin S (CatS) is upregulated in the progression of the disease and its inhibition has been proposed to be a promising pharmacological target to improve the prognosis of the disease condition. Asiatic acid and asiaticoside A are both pentacyclic triterpenoids isolated from Centella asiatica. Their use in treating various cardiovascular diseases has been reported. METHODS: In this study through in silico and in vitro methods, the pharmacokinetic properties, residue interaction, and inhibitory activities of these compounds were checked against the CatS enzyme. The SwissADME online package and the ToxTree 3.01 version of the offline software were used to determine the physicochemical properties of the compounds. RESULT: Asiatic acid reported no violation of the Lipinski rule while asiaticoside A violated the rule with regards to its molecular structure and size. The molecular docking was done using Molecular Operating Environment (MOE) and the S-score of - 7.25988, - 7.08466, and - 4.147913 Kcal/mol were recorded for LY300328, asiaticoside A, and asiatic acid respectively. Asiaticoside A has a docking score value (- 7.08466Kcal/mol) close to the co-crystallize compound. Apart from the close docking score, the amino acid residue glycine69 and asparagine163 both interact with the co-crystallized compound and asiaticoside A. The in vitro result clearly shows the inhibitory effect of asiaticoside and asiatic acid. Asiaticoside A has an inhibitory value of about 40% and asiatic acid has an inhibitory value of about 20%. CONCLUSION: This clearly shows that asiaticoside will be a better drug candidate than asiatic acid in inhibiting the CatS enzyme for the purpose of improving the outcome of atherosclerosis. However, certain modifications need to be made to the structural make-up of asiaticoside A to improve its pharmacokinetics properties.

Beet leaf (beta vulgaris L.) extract attenuates iron-induced testicular toxicity: Experimental and computational approach.

The purpose of this study was to investigate the protective effect of Beta vulgaris leaf extract (BVLE) on Fe2+-induced oxidative testicular damage via experimental and computational models. Oxidative testicular damage was induced via incubation of testicular tissue supernatant with 0.1 mM FeSO4 for 30 min at 37 °C. Treatment was achieved by incubating the testicular tissues with BVLE under the same conditions. The catalase (CAT), superoxide dismutase (SOD), glutathione (GSH), malondialdehyde (MDA), and nitric oxide (NO) levels, acetylcholinesterase (AChE), sodium-potassium adenosine triphosphatase (Na+/K + ATPase), ecto-nucleoside triphosphate diphosphohydrolase (ENTPDase), glucose-6-phosphatase (G6Pase), and fructose-1,6-bisphosphatase (F-1,6-BPase) were all measured in the tissues. We identified the bioactive compounds present using high-performance liquid chromatography (HPLC). Molecular docking and dynamic simulations were done on all identified compounds using a computational approach. The induction of testicular damage (p < 0.05) decreased the activities of GSH, SOD, CAT, and ENTPDase. In contrast, induction of testicular damage also resulted in a significant increase in MDA and NO levels and an increase in ATPase, G6Pase, and F-1,6-BPase activities. BVLE treatment (p < 0.05) reduced these levels and activities compared to control levels. An HPLC investigation revealed fifteen compounds in BVLE, with quercetin being the most abundant. The molecular docking and MDS analysis of the present study suggest that schaftoside may be an effective allosteric inhibitor of fructose 1,6-bisphosphatase based on the interacting residues and the subsequent effect on the dynamic loop conformation. These findings indicate that B. vulgaris can protect against Fe2+-induced testicular injury by suppressing oxidative stress, acetylcholinesterase, and purinergic activities while regulating carbohydrate dysmetabolism.

Discovery and biological evaluation of an adamantyl-amide derivative with likely MmpL3 inhibitory activity.

A series of molecules containing bulky lipophilic scaffolds was screened for activity against Mycobacterium tuberculosis and a number of compounds with antimycobacterial activity were identified. The most active compound, (2E)-N-(adamantan-1-yl)-3-phenylprop-2-enamide (C1), has a low micromolar minimum inhibitory concentration, low cytotoxicity (therapeutic index = 32.26), low mutation frequency and is active against intracellular Mycobacterium tuberculosis. Whole genome sequencing of mutants resistant to C1 showed a mutation in mmpL3 which may point to the involvement of MmpL3 in the antimycobacterial activity of the compound. In silico mutagenesis and molecular modelling studies were performed to better understand the binding of C1 within MmpL3 and the role that the specific mutation may play in the interaction at protein level. These analyses revealed that the mutation increases the energy required for binding of C1 within the protein translocation channel of MmpL3. The mutation also decreases the solvation energy of the protein, suggesting that the mutant protein might be more solvent-accessible, thereby restricting its interaction with other molecules. The results reported here describe a new molecule that may interact with the MmpL3 protein, providing insights into the effect of mutations on protein-ligand interactions and enhancing our understanding of this essential protein as a priority drug target.

Polymer drug conjugates containing memantine, tacrine and cinnamic acid: promising nanotherapeutics for the treatment of Alzheimer's disease.

AIM: To prepare polymer-drug conjugates containing a combination of memantine, tacrine, and E)-N-(3-aminopropyl)cinnamide, promising therapeutics for the treatment of neurodegenerative disorders. METHODS: The conjugates were characterised by 1HNMR, particle size analysis, SEM, LC-MS, TEM/EDX, and XRD, followed by in vitro anti-acetylcholinesterase and drug release studies. RESULTS: 1H NMR analysis revealed successful drug conjugation with drug mass percentages in the range of 1.3-6.0% w/w. The drug release from the conjugates was sustained for 10 h in the range of 20-36%. The conjugates' capability to inhibit acetylcholinesterase (AChE) activity was significant with IC50 values in the range of 13-44.4 µm which was more effective than tacrine (IC50 =1698.8 µm). The docking studies further confirmed that the conjugation of the drugs into the polymer improved their anti-acetylcholinesterase activity. CONCLUSION: The drug release profile, particle sizes, and in vitro studies revealed that the conjugates are promising therapeutics for treating neurodegenerative disorders.

Computational Target-Based Screening of Anti-MRSA Natural Products Reveals Potential Multitarget Mechanisms of Action through Peptidoglycan Synthesis Proteins.

Methicillin-resistant Staphylococcus aureus (MRSA) is one of the leading causes of bacterial infections in both healthcare and community settings. MRSA can acquire resistance to any current antibiotic, which has major implications for its current and future treatment options. As such, it is globally a major focus for infection control efforts. The mechanical rigidity provided by peptidoglycans in the bacteria cell walls makes it a promising target for broad-spectrum antibacterial drug discovery. The development of drugs that can target different stages of the synthesis of peptidoglycan in MRSA may compromise the integrity of its cell wall and consequently result in the rapid decline of diseases associated with this drug-resistant bacteria. The present study is aimed at screening natural products with known in vitro activities against MRSA to identify their potential to inhibit the proteins involved in the biosynthesis of the peptidoglycan cell wall. A total of 262 compounds were obtained when a literature survey was conducted on anti-MRSA natural products (AMNPs). Virtual screening of the AMNPs was performed against various proteins (targets) that are involved in the biosynthesis of the peptidoglycan (PPC) cell wall using Schrödinger software (release 2020-3) to determine their binding affinities. Nine AMNPs were identified as potential multitarget inhibitors against peptidoglycan biosynthesis proteins. Among these compounds, DB211 showed the strongest binding affinity and interactions with six protein targets, representing three stages of peptidoglycan biosynthesis, and thus was selected as the most promising compound. The MD simulation results for DB211 and its proteins indicated that the protein-ligand complexes were relatively stable over the simulation period of 100 ns. In conclusion, DB211 showed the potential to inhibit six proteins involved in the biosynthesis of the peptidoglycan cell wall in MRSA, thus reducing the chance of MRSA developing resistance to this compound. Therefore, DB211 provided a starting point for the design of new compounds that can inhibit multiple targets in the biosynthesis of the peptidoglycan layer in MRSA.

Alpha-Glucosidase and Alpha-Amylase Inhibitory Activities, Molecular Docking, and Antioxidant Capacities of Plectranthus ecklonii Constituents.

Shortage in insulin secretion or degradation of produced insulin is the principal characteristic of the metabolic disorder of diabetes mellitus (DM). However, because the current medications for the treatment of DM have many detrimental side effects, it is necessary to develop more effective antidiabetic drugs with minimal side effects. Alpha-glucosidase and alpha-amylase inhibitors are directly implicated in the delay of carbohydrate digestion. Pharmacologically, these inhibitors could be targeted for the reduction in glucose absorption rate and, subsequently, decreasing the postprandial rise in plasma glucose and the risk for long-term diabetes complications. The main objectives of this research study were to isolate different phytochemical constituents present in the methanolic extract of Plectranthusecklonii and evaluate their alpha-glucosidase and alpha-amylase inhibitory activities and antioxidant capacity. The phytochemical investigation of the methanolic extract of P. ecklonii yielded three known compounds, viz. parvifloron D, F, and G (1-3, respectively). Parvifloron G was isolated for the first time from P. ecklonii. The in vitro bio-evaluation of the methanolic extract of P. ecklonii and its isolated compounds against alpha-glucosidase showed that 3 exhibited moderate inhibitory activity with IC50 values of 41.3 ± 1.2 µg/mL. Molecular docking analysis confirmed the alpha-glucosidase inhibitory activity demonstrated by 3. Additionally, strong antioxidant capacities were demonstrated by 3 and 1 on ORAC (28726.1 ± 8.1; 3942.9.6.6 ± 0.1 µM TE/g), respectively, which were comparable with the reference antioxidant epigallocatechingallate (EGCG). Furthermore, 3 also showed strong activity on TEAC (3526.1 ± 0.6 µM TE/g), followed by 2 (1069.3 ± 2.4 µM TE/g), as well as on FRAP (1455.4 ± 2.0 µM AAE/g). The methanolic extract of P. ecklonii is a rich source of abietane diterpenes with strong antioxidant activities. This is the first scientific report on alpha-glucosidase and alpha-amylase inhibitory activities, molecular docking, and antioxidant capacities of P. ecklonii constituents.

In silico design, chemical synthesis and biological screening of novel 4-(1H)-pyridone-based antimalarial agents.

Identifying novel lead compounds in drug discovery has been challenging because of the rapid rise of drug resistance to the existing chemotherapeutics and a lack of understanding of complex metabolic pathways in the parasite. Integrating computational and experimental approaches has shown to be of great worth in identifying and developing novel promising pharmacophore hybrids. In this present research, a series of new 4-(1H)-pyridone-derived antimalarial agents were designed based on recent reports and our preliminary findings through in silico studies. Two of the 4-(1H)-Pyridone derivatives showed potential to bind to the Q0 site of the cytochrome bc1 complex and disrupt the mitochondrial electron transport chain. These compounds, along with previously synthesized compounds, exhibited significant inhibitory activities against the malaria parasite. Presently, seven compounds were successfully synthesized, characterized and these novel compounds have shown promise as antimalarial agents.

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.

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 five continents (Africa, Asia, Europe, South America, and North America) took part in the workshop. The workshop highlighted the potential applications of computational methodologies 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 hands-on 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 offered the opportunity to give oral presentations (15 min) and present posters in the form of flash presentations (5 min) upon submission of an abstract. The final program available on the workshop website ( https://caismd.indiayouth.info/ ) comprised of 4 keynote lectures (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.

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.

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.

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.

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.

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.

BACKGROUND: 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). METHODS: 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. RESULTS: A total of 1040 natural products were selected and a total of 13 molecular descriptors were analysed. Significant differences were observed among the sub-groups of NAA and CRAD for at least 11 of the molecular descriptors, including number of hydrogen bond donors and acceptors, molecular weight, polar and hydrophobic surface areas, chiral centres, oxygen and nitrogen atoms, and shape index. The remaining molecular descriptors, including clogP, number of rotatable bonds and number of aromatic rings, did not show any significant difference when comparing the two compound sets. Molecular similarity and chemical space analysis identified natural products that were structurally diverse from CRAD. Prediction of the pharmacokinetic properties and drug-likeness of these natural products identified over 50% with desirable drug-like properties. Nearly 70% of all natural products were identified as potentially promiscuous compounds. Structure-activity landscape analysis highlighted compound pairs that form 'activity cliffs'. In all, prioritization strategies for the NAA were proposed. CONCLUSIONS: Chemo-informatic profiling of NAA and CRAD have produced a wealth of information that may guide decisions and facilitate anti-malarial drug development from natural products. Articulation of the information provided within an interactive data-mining environment led to a prioritized list of NAA.