Assessing the pharmacokinetic profile of the CamMedNP natural products database: an in silico approach
© Ntie-Kang et al.; licensee Springer. 2013
Received: 2 July 2013
Accepted: 15 August 2013
Published: 30 August 2013
Drug metabolism and pharmacokinetic (DMPK) assessment has come to occupy a place of interest during the early stages of drug discovery today. Computer-based methods are slowly gaining ground in this area and are often used as initial tools to eliminate 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 the discovery of a drug.
In the present study, we present an in silico assessment of the DMPK profile of our recently published natural products database of 1,859 unique compounds derived from 224 species of medicinal plants from the Cameroonian forest. In this analysis, we have used 46 computed physico-chemical properties or molecular descriptors to predict the absorption, distribution, metabolism and elimination (ADME) of the compounds. This survey demonstrated that about 50% of the compounds within the Cameroonian medicinal plant and natural products (CamMedNP) database are compliant, having properties which fall within the range of ADME properties of >95% of currently known drugs, while >73% of the compounds have ≤2 violations. Moreover, about 72% of the compounds within the corresponding ‘drug-like’ subset showed compliance.
In addition to the previously verified levels of ‘drug-likeness’ and the diversity and the wide range of measured biological activities, the compounds in the CamMedNP database show interesting DMPK profiles and, hence, could represent an important starting point for hit/lead discovery from medicinal plants in Africa.
KeywordsADMET Database collection Descriptors In silico Medicinal plants Natural products
Natural products (NPs) play an increasingly important role in drug discovery today [1–5], both serving as drugs and as templates for the design of nature-inspired medicines [3, 6]. In fact, it has been reported that a significant proportion of drugs that undergo clinical trials are either naturally occurring or are derived from NPs . What characterises NPs are their richness in stereogenic centres and coverage of segments of chemical space which are typically not occupied by a majority of synthetic molecules and drugs [8, 9]. In addition, they generally contain more oxygen atoms and less aromatic atoms on average, when compared with ‘drug-like’ molecules [8–11]. It is needless to say that NPs sometimes fail the famous ‘drug-likeness’ test due to the often bulky nature of naturally occurring metabolites .
It is also worth mentioning that designing drug-like molecules having interesting pharmacokinetic properties is an important paradigm in drug discovery programs [12, 13]. This entails the search for lead compounds which can be easily orally absorbed, easily transported to their desired site of action, not easily attacked by metabolising enzymes so as to form toxic metabolic products before reaching the targeted site of action and easily eliminated from the body before accumulating in sufficient amounts that may produce adverse side effects. The ensemble of the above properties is often referred to as absorption, distribution, metabolism and elimination (ADME) properties, or better still ADMET or ADME/T or ADMETox (i.e. if toxicity criteria are also taken into consideration).
Computer-based in silico approaches for the prediction of ADMET profiles of drug leads at early stages of drug discovery are increasingly gaining ground [14–16]. This could be explained by the relative cost advantage added to the time factor, when compared to standard experimental approaches for ADMET profiling [17, 18]. On these grounds, several theoretical methods for the determination of ADMET parameters have been developed and implemented in a number of currently available software for drug discovery protocols [19–22], even though the predictions are sometimes disappointing . Such software often make use of quantitative structure-activity relationships [22–24] or knowledge-base methods [25–27]. The goal has been to considerably cut down on the currently very high cost of discovery of a drug . A promising lead is often defined as a compound which combines potency with an admirable ADMET profile. As such, compounds with unfavourably predicted pharmacokinetic profiles are either completely dismissed from the list of potential drug candidates (even if they prove to be highly potent) or the drug metabolism and pharmacokinetics (DMPK) properties are ‘fine tuned’ in order to improve their chances of making it to clinical trials . This explains why the ‘graveyard’ of very highly potent compounds which do not make it to clinical trials keeps filling up, to the extent that the process of drug discovery often presents the challenge of either resorting to new leads or ‘resurrecting’ some buried leads with the view of fine-tuning their ADMET profiles.
In a recent paper, we have presented a database of 1,859 compounds derived from the Cameroonian flora, Cameroonian medicinal plant and natural products (CamMedNP), the compounds being predicted to be sufficiently orally available and diverse to be employed in lead discovery programs . Additional arguments in favour of the use of this database are the wide range of the previously observed biological activities of the compounds and the wide range of ailments being treated by traditional medicine with the help of the herbs from which the compounds have been derived [29, 30].
Numerous drugs at a late stage of pharmaceutical development and many more lead compounds fail due to adverse pharmacokinetic properties . It is, therefore, important to incorporate the prediction of the ADME properties into the lead compound selection, by means of molecular descriptors. A molecular descriptor is often defined as a structural or physico-chemical property of a molecule or part of a molecule, for example the logarithm of the n-octanol/water partition coefficient (log P), molar weight (MW) and total polar surface area. A number of relevant molecular properties (descriptors) are often used to help predict the pharmacokinetic behaviour of potential drug leads. In the present study, we have carried out an in silico assessment of the ADMET profile of the CamMedNP database by the use of computed molecular descriptors currently implemented in a wide range of software tools as indicators of the pharmacokinetic properties of a large proportion of currently known drugs.
Data sources and generation of 3D structures
The plant sources, geographical collection sites, chemical structures of pure compounds and their measured biological activities were retrieved from literature sources and have been previously described . The three-dimensional (3D) structures were generated using the builder module of MOE , and energy minimization was subsequently carried out using the MMFF94  until a gradient of 0.01 kcal/mol was reached.
Initial treatment of chemical structures and calculation of ADMET-related descriptors
The 1,859 low-energy 3D chemical structures in the CamMedNP library were saved in mol2 format and initially treated with LigPrep , distributed by Schrodinger, Inc. (New York, USA). This implementation was carried out with the graphical user interface of the Maestro software package (New York, USA) , using the OPLS force field [35–37]. Protonation states at biologically relevant pH were correctly assigned (group I metals in simple salts were disconnected, strong acids were deprotonated and strong bases protonated, while topological duplicates and explicit hydrogens were added). All molecular modelling was carried out on a Linux workstation (San Francisco, USA) with a 3.5 GHz Intel Core2 Duo processor (Santa Clara, USA). A set of the ADMET-related properties (a total of 46 molecular descriptors) were calculated using the QikProp program (New York, USA)  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 within the CamMedNP library. The #stars parameter indicates the number of property descriptors computed by QikProp, which falls outside the optimum range of values for 95% of known drugs. The methods implemented were developed by Jorgensen et al. [38–40]. Among the calculated descriptors are the total solvent-accessible molecular surface, S mol in Å2 (probe radius 1.4 Å; range for 95% of drugs is 300 to 1,000 Å2); the hydrophobic portion of the solvent-accessible molecular surface, S mol,hfob in Å2 (probe radius 1.4 Å; range for 95% of drugs is 0 to 750 Å2); the total volume of molecule enclosed by solvent-accessible molecular surface, V mol in Å3 (probe radius 1.4 Å; range for 95% of drugs is 500 to 2,000 Å3); the logarithm of aqueous solubility, logS wat (range for 95% of drugs is −6.0 to 0.5) [36, 38]; the logarithm of predicted binding constant to human serum albumin, logK HSA (range for 95% of drugs is −1.5 to 1.2) ; the logarithm of predicted blood/brain barrier partition coefficient, log B/B (range for 95% of drugs is −3.0 to 1.0) [42–44]; the predicted apparent Caco-2 cell membrane permeability (BIP Caco-2) in Boehringer-Ingelheim scale, in nm/s (range for 95% of drugs is <5 low, >100 high) [45–47]; the predicted apparent Madin-Darby canine kidney (MDCK) cell permeability in nm s−1 (<25 poor, >500 great) ; the index of cohesion interaction in solids, Indcoh, calculated from the number of hydrogen bond acceptors (HBA), hydrogen bond donors (HBD) and the surface area accessible to the solvent (S mol) by the relation (0.0 to 0.05 for 95% of drugs) ; the globularity descriptor, Glob = (4πr2)/S mol, where r is the radius of the sphere whose volume is equal to the molecular volume (0.75 to 0.95 for 95% of drugs); the predicted polarizability, QPpolrz (13.0 to 70.0 for 95% of drugs); the predicted IC50 value for blockage of HERG K+ channels, logHERG (concern <−5) [48, 49]; the predicted skin permeability, logK p (−8.0 to −1.0 for 95% of drugs) [50, 51]; and the number of likely metabolic reactions, #metab (range for 95% of drugs is 0 to 15).
Results and discussion
Overall DMPK compliance of the CamMedNP library
Average pharmacokinetic property distributions of total CamMedNP library in comparison with various subsets
BIP Caco-2 (nm s−1)i
S mol (Å2)j
S mol,hfob (Å2)k
V mol (Å3)l
LogS wat (S in mol L−1)m
LogK HSA n
LogK p t
Percentage compliances of selected ADMET-related descriptors of total CamMedNP library in comparison with various subsets
BIP Caco-2 (nm s−1)
S mol (Å2)
S mol,hfob (Å2)
V mol (Å3)
LogS wat (S in mol L−1)
The size of a molecule, as well as its capacity to make hydrogen bonds, its overall lipophilicity, its shape and flexibility are important properties to consider when determining permeability. Molecular flexibility has been seen as a parameter which is dependent on the number of rotatable bonds (NRB), a property which influences the bioavailability in rats . The distribution of the NRB for this dataset has been previously discussed  and revealed that the compounds within the CamMedNP library show some degree of conformational flexibility, the peak value for the NRB being between 1 and 2, while the average value is 5.31 (Table 1).
Prediction of blood-brain barrier penetration
Prediction of dermal penetration
This parameter showed variations from 0 to 1,603 μ cm−2 h−1, with only about 1.39% of the compounds in CamMedNP having the predicted value of J m > 100 μ cm−2 h−1.
Prediction of plasma-protein binding
Prediction of blockage of human ether-a-go-go-related gene potassium channel
Usefulness of the CamMedNP library
The usefulness of the CamMedNP database in lead generation has been exemplified with the docking and pharmacophore-based screening for potential inhibitors of a validated anti-malarial drug target in our laboratory, and the results will be published in a subsequent paper. It is important to mention that virtual screening results could provide insight and direct natural products chemists to search for theoretically active principles with attractive ADMET profiles, which have been previously isolated, but not tested for activity against specified drug targets (if samples are absent). This ‘resurrection’ process could prove to be a better procedure for lead search than the random screening, which is a common practice in our Cameroonian laboratories. CamMedNP is constantly being updated; meanwhile, a MySQL platform (Cupertino, USA) to facilitate the searching of this database and ordering of compound samples is under development within our group and will also be published subsequently. However, 3D structures of the compounds, as well as their physico-chemical properties that were used to evaluate the DMPK profile, can be freely downloaded as additional files accompanying this publication (see Additional file 1, Additional file 2, Additional file 3, Additional file 4). In addition, information about compound sample availability can be obtained on request from the authors of this paper or from the pan-African Natural Products Library (p-ANAPL) project [63, 64].
Modern drug discovery programs usually involve the search for small molecule leads with attractive pharmacokinetic profiles. The presence of such within the CamMedNP library is of major importance and, therefore, renders the database attractive, in addition to the already-known properties (drug-like, lead-like fragment-like and diverse). This is an indication that the 3D structures of naturally occurring compounds within the CamMedNP could be a good starting point for docking, neural networking and pharmacophore-based virtual screening campaigns, thus rendering the CamMedNP as a useful asset for the drug discovery community. 3D structures of the compounds, as well as their physico-chemical properties that were used to evaluate the DMPK profile of the CamMedNP library, can be freely downloaded (for non-commercial use) as additional files which accompany this publication (see Additional file 1, Additional file 2, Additional file 3, Additional file 4). The physical samples for testing are available at the various research laboratories in Cameroon in varying quantities. Questions regarding the availability of the compound samples could be addressed directly to the authors of this paper. Otherwise, the samples could be obtainable from the p-ANAPL consortium, which has a mandate to collect samples of NPs from the entire continent of Africa and make them available for biological screening. This network is being set up under the auspices of the Network for Analytical and Bioassay Services in Africa [63, 64].
WS and SMNE are professors of Medicinal Chemistry with an interest in CADD, while SMNE also focuses on organic synthesis and on natural product leads from the Cameroonian medicinal plants. LMM and JAM are natural products chemists actively involved in the isolation and characterization of secondary metabolites from the Cameroonian medicinal plants. LLL holds a PhD in Environmental Science and manages the Chemical and Bioactivity Information Centre (CBIC) with a focus on developing databases for information from medicinal herbs in Africa. PNJ is a retired research officer of Lhasa Ltd., who currently leads the CBIC branch in Leeds, UK. FNK is a PhD student working on CADD under the joint supervision of LCOO and EM.
Absorption distribution, metabolism, excretion and toxicity
Cameroonian medicinal plant and natural products database
Drug metabolism and pharmacokinetics
- log P:
logarithm of the octan-1-ol/water partition coefficient
Madin-Darby canine kidney
Number of rotatable bonds
pan-African natural products library
Financial support is acknowledged from the German Academic Exchange Service (DAAD) to FNK for his stay in Halle, Germany for part of his PhD and from the International Centre for Theoretical Physics (ICTP), Trieste, Italy, via the OEA-AC71 Program. The authors also acknowledge the academic licence generously offered by Schrodinger Inc., for this work. The assistance of Mr. Vincent de Paul Nzuwah Nziko (graduate student, Chemistry and Biochemistry, Utah State University, USA) is also acknowledged for proofreading the manuscript.
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