The potential of anti-malarial compounds derived from African medicinal plants, part III: an in silico evaluation of drug metabolism and pharmacokinetics profiling
© Onguéné et al.; licensee Springer. 2014
Received: 14 April 2014
Accepted: 26 June 2014
Published: 5 September 2014
Malaria is an endemic disease affecting many countries in Tropical regions. In the search for compound hits for the design and/or development of new drugs against the disease, many research teams have resorted to African medicinal plants in order to identify lead compounds. Three-dimensional molecular models were generated for anti-malarial compounds of African origin (from 'weakly' active to 'highly' active), which were identified from literature sources. Selected computed molecular descriptors related to absorption, distribution, metabolism, excretion and toxicity (ADMET) of the phytochemicals have been analysed and compared with those of known drugs in order to access the 'drug-likeness' of these compounds.
In the present study, more than 500 anti-malarial compounds identified from 131 distinct medicinal plant species belonging to 44 plant families from the African flora have been considered. On the basis of Lipinski's 'Rule of Five', about 70% of the compounds were predicted to be orally bioavailable, while on the basis of Jorgensen's 'Rule of Three', a corresponding >80% were compliant. An overall drug-likeness parameter indicated that approximately 55% of the compounds could be potential leads for the development of drugs.
From the above analyses, it could be estimated that >50% of the compounds exhibiting anti-plasmodial/anti-malarial activities, derived from the African flora, could be starting points for drug discovery against malaria. The 3D models of the compounds have been included as an accompanying file and could be employed in virtual screening.
KeywordsAfrica Malaria Medicinal plants Metabolism Natural products Pharmacokinetics
Malaria is an endemic disease which affects vast proportions of the populations of most Tropical countries (covering Africa, Asia and Latin America) ,. The disease condition is caused by protozoans of the Plasmodium genus, mostly Plasmodium falciparum. Statistics show that about half of the world's population is at risk of contracting malaria and that 1 to 2 million annual deaths (mostly amongst African children) can be attributed to malaria alone -. In addition, the spread of the disease has been enhanced by the development of resistance in the anopheline vector against standard insecticides, amongst other factors, which have not unfortunately been put under check .
One promising way to fight malaria is to search for vaccines and new drugs, since no vaccine has yet been put in the market and the disease-causing parasites have developed resistant strains against existing chemotherapies -. It should however be mentioned that the process of discovering a drug is quite timely and costly . One of the current approaches for shortening the time required and cutting down the cost for the discovery of lead compounds which potentially inhibit or modulate known drug targets is to incorporate computer-based methods like docking techniques, pharmacophore-based searches and neural networking -. Computer-based methods have also been incorporated in the prediction of likely metabolic pathways of drug molecules, as well as predict their pharmacokinetic profiles -. The absorption, distribution, metabolism, excretion and toxicity (ADMET) profile of a potential drug molecule should be known if it has to stand the chances of entering the market. Hence, assessing such information for lead compounds early enough would help eliminate molecules with predicted uninteresting profiles and eventually cut down the price of drug discovery .
With the accumulation of 'wet lab' biodata on drug metabolism and pharmacokinetics (DMPK) by the close of the 1990s, pharmaceutical companies are increasingly switching over to the use of statistical and knowledge-based methods, implemented in computer software, in the prediction of ADMET/DMPK properties of drug leads, in contrast to the former approach which is more costly and time consuming -. In our quest to assess the potential of natural products (NPs) derived from African medicinal plants for the development of anti-malarial drugs ,, an in silico approach based on computed molecular descriptors has been carried out, in comparison with those of known drugs, as previously described in the literature -. In this paper, we present a computer-based DMPK analysis of >500 anti-malarial compounds, which have been previously isolated from the African flora.
The plant sources, geographical collection sites, chemical structures of pure compounds as well as their spectroscopic data were retrieved from literature sources comprising of MSc theses, PhD theses, textbook chapters and journal articles, with references ranging from 1971 to 2013. A full list of journals consulted is given in the supplementary material (Additional file 1). By convention, activities were categorized into 'very potent', 'good', 'good to moderate', 'weak', 'very weak' and 'inactive'. Following the criteria used by Mahmoudi et al.  and Wilcox et al. , a pure compound was considered highly active if IC50 < 0.06 μM, being active with 0.06 μM ≤ IC50 ≤ 5 μM, weakly active when 5 μM ≤ IC50 ≤ 10 μM, and compounds with IC50 > 10 μM were considered inactive. The following inhibition percentages were proposed for in vivo activity of anti-malarial extracts at a fixed dose of 250 mg kg−1 day−1: 100% to 90% (very good activity), 90% to 50% (good to moderate), 50% to 10% (moderate to weak) and 0% (inactive) .
Generation of 3D models, optimization and correction of protonation states
The 2D structures of the compounds were retrieved from the literature sources, and all 3D molecular models were generated using the graphical user interface (GUI) of the MOE software  running on a Linux workstation with a 3.5 GHz Intel Core2 Duo processor. The 3D structures were generated using the builder module of MOE, and energy minimization was subsequently carried out using the MMFF94 force field  until a gradient of 0.01 kcal mol−1 was reached. The 3D structures of the compounds were then saved as .mol2 files subsequently treated with LigPrep . This implementation was carried out with the GUI of the Maestro software package , using the optimized potentials for liquid simulations (OPLS) forcefield -. Protonation states at biologically relevant pH were correctly assigned (group I metals in simple salts were disconnected, strong acids were deprotonated, strong bases protonated, while topological duplicates and explicit hydrogens were added). The generated 3D models have been included in the supplementary material (Additional file 2).
Calculation of molecular descriptors
A set of ADMET-related properties (a total of 46 molecular descriptors) were calculated by using the QikProp program  running in normal mode. QikProp generates physically relevant descriptors and uses them to perform ADMET predictions. An overall ADME-compliance score - drug-likeness parameter (indicated by #stars) - was used to assess the pharmacokinetic profiles of the compounds. The #stars parameter indicates the number of property descriptors computed by QikProp that fall outside the optimum range of values for 95% of known drugs. The methods implemented were developed by Jorgensen and Duffy -. Some of the computed ADMET descriptors are shown in Additional file 3: Table S1, along with their significance in DMPK profiling and the recommended ranges for 95% of known drugs.
Results and discussion
Plant families and compound types
Significance of selected computed molecular descriptors
The overall drug-likeness of a molecule is often determined by the #stars parameter, which depends on 24 computed parameters (descriptors) of a molecule with respect to the recommended range for 95% of known drugs (Additional file 3: Table S1). A #star = 0 corresponds to an ideally drug-like molecule, while #stars = n indicates that a given molecule has n non-compliant descriptors (values fall outside the recommended range for 95% of known drugs). The solubility of a drug, evaluated by the model of Jorgensen and Duffy ,, determines the bioavailability of a drug to an extent, since the bioavailability of a compound depends on the processes of absorption and liver first-pass metabolism . Absorption in turn depends on the solubility and permeability of the compound, as well as interactions with transporters and metabolizing enzymes in the gut wall.
The efficiency and distribution of a drug may be affected by the degree to which it binds to the proteins within blood plasma. When a drug binds to plasma proteins (like human serum albumin, lipoprotein, glycoprotein, α, β, and γ globulins), the quantity of the drug in general blood circulation is greatly reduced and hence the less bound a drug is, the more efficiently it can traverse cell membranes. The predicted plasma-protein binding has been estimated by the prediction of binding to human serum albumin; the log KHSA parameter (recommended range is −1.5 to 1.5 for 95% of known drugs). The number of metabolic steps (#metab) also determines whether the molecules can easily gain access to the target site after entering the blood stream.
The toxicity parameter is often predicted by the logarithm of IC50 values for blockage of the human ether-a-go-go related gene (HERG). HERG encodes a potassium ion (K+) channel that is implicated in the fatal arrhythmia known as torsade de pointes or the long QT syndrome . The HERG K+ channel is best known for its contribution to the electrical activity of the heart that coordinates the heart's beating. It therefore appears to be the molecular target responsible for the cardiac toxicity of a wide range of therapeutic drugs . HERG has also been associated with modulating the functions of some cells of the nervous system and with establishing and maintaining cancer-like features in leukemic cells . Thus, HERG K+ channel blockers are potentially toxic and the predicted IC50 values often provide reasonable predictions for cardiac toxicity of drugs in the early stages of drug discovery .
Lipinski's criteria for evaluation of oral bioavailability
Minimum, maximum and mean values for computed Lipinski parameters
Jorgensen's criteria for assessment of oral bioavailability
Mean values and percentage compliances of selected ADMET-related descriptors for 511 anti-malarial compounds from African medicinal plants
BIPCaco-2 (nm s−1)
Smol, hfob (Å2)
log Swat (S in mol L−1)
Assessment of the overall DMPK compliance by the drug-likeness parameter
Prediction of drug distribution and binding to human serum albumin and dermal penetration parameters
The skin permeability parameter showed 93.3% compliance with 95% of drugs. The distribution curve was a Gaussian-shape centred on −2.5 log Kp units (Additional file 3: Figure S1), while the predicted maximum transdermal transport rates, Jm (in μ cm−2 h−1), were estimated to vary from 0 to about 1,640 units, with only five compounds having predicted value of Jm > 100 μ cm−2 h−1.
Prediction of blood/brain barrier penetration and activity in the central nervous system
Access to the central nervous system was simulated by the log B/B parameter, while activity in the central nervous system was estimated by the CNS parameter (on the −2 = inactive and +2 = active scale). The log B/B plot (Figure 6B) showed a sharp peak at 0.5 log B/B units, with 93.3% compliance with 95% of drugs. On the other hand, only 3.5% of the compounds were estimated to have an activity in the central nervous system.
Prediction of number of likely metabolic reactions
The number of likely metabolic steps was also computed by QikProp and plotted against the counts (Figure 6C). This parameter is often used to assess the likelihood of a molecule to easily gain access to the target site after entering the blood stream. The average estimated number of possible metabolic reactions was between 5 and 6, even though some of the compounds are likely to undergo as many as up to 16 metabolic reactions due to the complexity of some of the plant secondary metabolites. About 84% of the compounds are predicted to undergo the recommended number of metabolic steps (1 to 8 reactions),
Prediction of toxicity by blockage of HERG K+ channel
In this study, the calculated physicochemical properties and indicators of drug-likeness have been used in the assessment of the ADMET/DMPK profiles of >500 compounds isolated from medicinal plants in Africa, which have exhibited from weak to high in vitro and/or in vivo anti-plasmodial/anti-malarial activity. The overall estimate gave good compliance with the computed parameters for 95% of known drugs, as well as with Lipinski' drug-likeness criteria. The results give good reason for further investigation of the suitability of anti-malarial compounds derived from African flora to be directly employed as drugs or as lead compounds from which new anti-malarial drugs could be developed. The generated 3D structures of the compounds have been included as a supplementary file (Additional file 2), while enquiries for the availability of compounds for screening purposes could be addressed to the p-ANAPL consortium , which has the mandate to collect compound samples from African flora for biological screening purposes.
absorption, distribution, metabolism, excretion and toxicity
drug metabolism and pharmacokinetics
hydrogen bond acceptors
hydrogen bond donors
- log P:
logarithm of the octan-1-ol/water partition coefficient
Madin-Darby canine kidney
number of rotatable bonds
Financial support is acknowledged from Lhasa Ltd, Leeds, UK through the Chemical and Bioactivity Information Centre (CBIC), University of Buea, Cameroon. Schrodinger Inc. is acknowledged for the academic license.
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