BackgroundIn Cameroon herbs are traditionally used to meet health care needs and plans are on the way to integrate traditional medicine in the health care system, even though the plans have not been put into action yet. The country however has a rich biodiversity, with ~8,620 plant species, some of which are commonly used in the treatment of several microbial infections and a range of diseases (malaria, trypanosomiasis, leishmaniasis, diabetes and tuberculosis).MethodsOur survey consisted in collecting published data from the literature sources, mainly from PhD theses in Cameroonian university libraries and also using the author queries in major natural product and medicinal chemistry journals. The collected data includes plant sources, uses of plant material in traditional medicine, plant families, region of collection of plant material, isolated metabolites and type (e.g. flavonoid, terpenoid, etc.), measured biological activities of isolated compounds, and any comments on significance of isolated metabolites on the chemotaxonomic classification of the plant species. This data was compiled on a excel sheet and analysed.ResultsIn this study, a literature survey led to the collection of data on 2,700 secondary metabolites, which have been previously isolated or derived from Cameroonian medicinal plants. This represents distinct phytochemicals derived from 312 plant species belonging to 67 plant families. The plant species are investigated in terms of chemical composition with respect to the various plant families. A correlation between the known biological activities of isolated compounds and the ethnobotanical uses of the plants is also attempted. Insight into future direction for natural product search within the Cameroonian forest and Savanna is provided.ConclusionsIt can be verified that a phytochemical search of active secondary metabolites, which is inspired by knowledge from the ethnobotanical uses of medicinal plants could be very vital in a drug discovery program from plant-derived bioactive compounds.

BackgroundComputer-aided drug design (CADD) often involves virtual screening (VS) of large compound datasets and the availability of such is vital for drug discovery protocols. We present CamMedNP - a new database beginning with more than 2,500 compounds of natural origin, along with some of their derivatives which were obtained through hemisynthesis. These are pure compounds which have been previously isolated and characterized using modern spectroscopic methods and published by several research teams spread across Cameroon.DescriptionIn the present study, 224 distinct medicinal plant species belonging to 55 plant families from the Cameroonian flora have been considered. About 80 % of these have been previously published and/or referenced in internationally recognized journals. For each compound, the optimized 3D structure, drug-like properties, plant source, collection site and currently known biological activities are given, as well as literature references. We have evaluated the “drug-likeness” of this database using Lipinski’s “Rule of Five”. A diversity analysis has been carried out in comparison with the ChemBridge diverse database.ConclusionCamMedNP could be highly useful for database screening and natural product lead generation programs.

We have carried out a computational structure-based design of new potent pyrrolidine carboxamide (PCAMs) inhibitors of enoyl-acyl carrier protein reductase (InhA) of Mycobacterium tuberculosis (MTb). Three-dimensional (3D) models of InhA-PCAMx complexes were prepared by in situ modification of the crystal structure of InhA-PCAM1 (Protein Data Bank (PDB) entry code: 4U0J), the reference compound of a training set of 20 PCAMs with known experimental inhibitory potencies (IC50exp). First, we built a gas phase quantitative structure-activity relationships (QSAR) model, linearly correlating the computed enthalpy of the InhA-PCAM complex formation and the IC50exp. Further, taking into account the solvent effect and loss of inhibitor entropy upon enzyme binding led to a QSAR model with a superior linear correlation between computed Gibbs free energies (ΔΔGcom) of InhA-PCAM complex formation and IC50exp (pIC50exp = −0.1552·ΔΔGcom + 5.0448, R2 = 0.94), which was further validated with a 3D-QSAR pharmacophore model generation (PH4). Structural information from the models guided us in designing of a virtual combinatorial library (VL) of more than 17 million PCAMs. The VL was adsorption, distribution, metabolism and excretion (ADME) focused and reduced down to 1.6 million drug like orally bioavailable analogues and PH4 in silico screened to identify new potent PCAMs with predicted IC50pre reaching up to 5 nM. Combining molecular modeling and PH4 in silico screening of the VL resulted in the proposed novel potent antituberculotic agent candidates with favorable pharmacokinetic profiles.