It has long been recognised that the ability to predict the metabolic fate of a chemical substance and the potential toxicity of either the parent compound or its metabolites are important in novel drug design. The popularity of using computer models as an aid in this area has grown considerably in recent years. LHASA Limited has been developing knowledge-based expert systems for toxicity and metabolism prediction in collaboration with industry and regulatory authorities. These systems, DEREK, StAR and METEOR, use rules to describe the relationship between chemical structure and either toxicity in the case of DEREK and StAR, or metabolic fate in the case of METEOR. The rule refinement process for DEREK often involves assessing the predictions for a novel set of compounds and comparing them to their biological assay results as a measure of the system's performance. For example, 266 non-congeneric chemicals from the National Toxicology Program database have been processed through the DEREK mutagenicity knowledge base and the predictions compared to their Salmonella typhimurium mutagenicity data. Initially, 81 of 114 mutagens (71%) and 117 of 152 non-mutagens (77%) were correctly identified. Following further knowledge base development, the number of correctly identified mutagens has increased to 96 (84%). Further work on improving the predictive capabilities of DEREK, StAR and METEOR is in progress.

No abstract

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.

Traditional medicinal practices play a key role in health care systems in countries with developing economies. The aim of this survey was to validate the use of traditional medicine within local Nigerian communities. In this review, we examine the ethnobotanical uses of selected plant species from the Nigerian flora and attempt to correlate the activities of the isolated bioactive principles with known uses of the plant species in African traditional medicine. Thirty-three (33) plant species were identified and about 100 out of the 120 compounds identified with these plants matched with the ethnobotanical uses of the plants.

To be useful, a system which predicts the metabolic fate of a chemical should predict the more likely metabolites rather than every possibility. Reasoning can be used to prioritize biotransformations, but a real biochemical domain is complex and cannot be fully defined in terms of the likelihood of events. This paper describes the combined use of two models for reasoning under uncertainty in a working system, METEOR-one model deals with absolute reasoning and the second with relative reasoning.

PurposeDrug metabolism and pharmacokinetics (DMPK) assessment has come to occupy a place of interest during the early stages of drug discovery today. The use of computer modelling to predict the DMPK and toxicity properties of a natural product library derived from medicinal plants from Central Africa (named ConMedNP). Material from some of the plant sources are currently employed in African Traditional Medicine.MethodsComputer-based methods are slowly gaining ground in this area and are often used as preliminary criteria for the elimination of compounds likely to present uninteresting pharmacokinetic profiles and unacceptable levels of toxicity from the list of potential drug candidates, hence cutting down the cost of discovery of a drug.In the present study, we present an in silico assessment of the DMPK and toxicity profile of a natural product library containing ~3,200 compounds, derived from 379 species of medicinal plants from 10 countries in the Congo Basin forests and savannas, which have been published in the literature. In this analysis, we have used 46 computed physico-chemical properties or molecular descriptors to predict the absorption, distribution, metabolism and elimination and toxicity (ADMET) of the compounds.ResultsThis survey demonstrated that about 45% of the compounds within the ConMedNP compound library are compliant, having properties which fall within the range of ADME properties of 95% of currently known drugs, while about 69% of the compounds have ≤ 2 violations. Moreover, about 73% of the compounds within the corresponding “drug-like” subset showed compliance.ConclusionsIn addition to the verified levels of “drug-likeness”, diversity and the wide range of measured biological activities, the compounds from medicinal plants in Central Africa show interesting DMPK profiles and hence could represent an important starting point for hit/lead discovery.Electronic supplementary materialThe online version of this article (doi:10.1186/2193-9616-1-12) contains supplementary material, which is available to authorized users.

We have made available a database of over 1 billion compounds predicted to be easily synthesizable, called Synthetically Accessible Virtual Inventory (SAVI). They have been created by a set of transforms based on an adaptation and extension of the CHMTRN/PATRAN programming languages describing chemical synthesis expert knowledge, which originally stem from the LHASA project. The chemoinformatics toolkit CACTVS was used to apply a total of 53 transforms to about 150,000 readily available building blocks (enamine.net). Only single-step, two-reactant syntheses were calculated for this database even though the technology can execute multi-step reactions. The possibility to incorporate scoring systems in CHMTRN allowed us to subdivide the database of 1.75 billion compounds in sets according to their predicted synthesizability, with the most-synthesizable class comprising 1.09 billion synthetic products. Properties calculated for all SAVI products show that the database should be well-suited for drug discovery. It is being made publicly available for free download from https://doi.org/10.35115/37n9-5738.

The paper begins with a discussion of the goals of metabolic predictions in early drug research, and some difficulties toward this objective, mainly the various substrate and product selectivities characteristic of drug metabolism. The major in silico approaches to predict drug metabolism are then classified and summarized. A discrimination is, thus, made between 'local' and 'global' systems. In its second part, an evaluation of METEOR, a rule-based expert system used to predict the metabolism of drugs and other xenobiotics, is reported. The published metabolic data of ten substrates were used in this evaluation, the overall results being discussed in terms of correct vs. disputable (i.e., false-positive and false-negative) predictions. The predictions for four representative substrates are presented in detail (Figs. 1-4), illustrating the interest of such an evaluation in identifying where and how predictive rules can be improved.