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Modeling the local absorption and retention patterns of membrane-permeant small molecules in a cellular context could facilitate development of site-directed chemical agents for bioimaging or therapeutic applications. Here, we present an integrative approach to this problem, combining in silico computational models, in vitro cell based assays and in vivo biodistribution studies. To target small molecule probes to the epithelial cells of the upper airways, a multiscale computational model of the lung was first used as a screening tool, in silico. Following virtual screening, cell monolayers differentiated on microfabricated pore arrays and multilayer cultures of primary human bronchial epithelial cells differentiated in an air-liquid interface were used to test the local absorption and intracellular retention patterns of selected probes, in vitro. Lastly, experiments involving visualization of bioimaging probe distribution in the lungs after local and systemic administration were used to test the relevance of computational models and cell-based assays, in vivo. The results of in vivo experiments were consistent with the results of in silico simulations, indicating that mitochondrial accumulation of membrane permeant, hydrophilic cations can be used to maximize local exposure and retention, specifically in the upper airways after intratracheal administration.

Author Summary

We have developed an integrative, cell-based modeling approach to facilitate the design and discovery of chemical agents directed to specific sites of action within a living organism. Here, a computational, multiscale transport model of the lung was adapted to enable virtual screening of small molecules targeting the epithelial cells of the upper airways. In turn, the transport behaviors of selected candidate probes were evaluated to establish their degree of retention at a site of absorption, using computational simulations as well as two in vitro cell-based assay systems. Lastly, bioimaging experiments were performed to examine candidate molecules' distribution in the lungs of mice after local and systemic administration. Based on computational simulations, the higher mitochondrial density per unit absorption surface area is the key parameter determining the higher retention of small molecule hydrophilic cations in the upper airways, relative to lipophilic weak bases, specifically after intratracheal administration.

In the body, cell monolayers serve as permeability barriers, determining transport of drug molecules from one organ or tissue compartment to another. After oral administration, for example, drug transport across the epithelial cell monolayer lining the lumen of the intestine determines the fraction of drug in the gut that is absorbed by the body. By modeling passive transcellular transport properties in the presence of an apical to basolateral concentration gradient, we demonstrate how a computational, cell-based molecular transport simulator can be used to define a physicochemical property space occupied by molecules with desirable permeability and intracellular retention characteristics. Considering extracellular domains of cell surface receptors located on the opposite side of a cell monolayer as a drug’s desired site-of-action, simulation of transcellular transport can be used to define the physicochemical properties of molecules with maximal transcellular permeability but minimal intracellular retention. Arguably, these molecules would possess very desirable features: least likely to exhibit non-specific toxicity, metabolism and side effects associated with high (undesirable) intracellular accumulation; and, most likely to exhibit favorable bioavailability and efficacy associated with maximal rates of transport across cells and minimal intracellular retention, resulting in (desirable) accumulation at the extracellular site-of-action. Calculated permeability predictions showed good correlations with PAMPA, Caco2, and intestinal permeability measurements, without “training” the model and without resorting to statistical regression techniques to “fit” the data. Therefore, cell-based molecular transport simulators could be useful in silico screening tools for chemical genomics and drug discovery.

Peptides are of great therapeutic potential as vaccines and drugs. Knowledge of physicochemical descriptors, including the partition coefficient P (commonly expressed in logarithm form: logP), is useful for screening out unsuitable molecules and also for the development of predictive Quantitative Structure-Activity Relationships (QSARs). In this paper we develop a new approach to the prediction of LogP values for peptides based on an empirical relationship between global molecular properties and measured physical properties. Our method was successful in terms of peptide prediction (total r2 = 0.641). The final model consisted of 5 physicochemical descriptors (molecular weight, number of single bonds, 2D-VDW volume, 2D-VSA hydrophobic and 2D-VSA polar). The approach is peptide specific and its predictive accuracy was high. Overall, 67% of the peptides were able to be predicted within +/-0.5 log units from the experimental values. Our method thus represents a novel prediction method with proven predictive ability.

As part of our effort to increase survival of drug candidates and to move our medicinal chemistry design to higher probability space for success in the Neuroscience therapeutic area, we embarked on a detailed study of the property space for a collection of central nervous system (CNS) molecules. We carried out a thorough analysis of properties for 119 marketed CNS drugs and a set of 108 Pfizer CNS candidates. In particular, we focused on understanding the relationships between physicochemical properties, in vitro ADME (absorption, distribution, metabolism, and elimination) attributes, primary pharmacology binding efficiencies, and in vitro safety data for these two sets of compounds. This scholarship provides guidance for the design of CNS molecules in a property space with increased probability of success and may lead to the identification of druglike candidates with favorable safety profiles that can successfully test hypotheses in the clinic.

A biophysical, computational model of cell pharmacokinetics (1CellPK) is being developed to enable prediction of the intracellular accumulation and transcellular transport properties of small molecules using their calculated physicochemical properties as input. To test if 1CellPK can generate accurate, quantitative hypotheses and guide experimental analysis of the transcellular transport kinetics of small molecules, epithelial cells were grown on impermeable polyester membranes with cylindrical pores and chloroquine (CQ) was used as a transport probe. The effect of the number of pores and their diameter on transcellular transport of CQ was measured in apical-to-basolateral or basolateral-to-apical directions, at pH 7.4 and 6.5 in the donor compartment. Experimental and simulation results were consistent with a phospholipid bilayer-limited, passive diffusion transport mechanism. In experiments and 1CellPK simulations, intracellular CQ mass and the net rate of mass transport varied <2-fold although total pore area per cell varied >10-fold, so by normalizing the net rate of mass transport by the pore area available for transport, cell permeability on 3µm pore diameter membranes was more than an order of magnitude less than on 0.4µm pore diameter membranes. The results of simulations of transcellular transport were accurate for the first four hours of drug exposure, but those of CQ mass accumulation were accurate only for the first five minutes. Upon prolonged incubation, changes in cellular parameters such as lysosome pH rise, lysosome volume expansion, and nuclear shrinkage were associated with excess CQ accumulation. Based on the simulations, lysosome volume expansion alone can partly account for the measured, total intracellular CQ mass increase, while adding the intracellular binding of the protonated, ionized forms of CQ (as reflected in the measured partition coefficient of CQ in detergent-permeabilized cells at physiological pH) can further improve the intracellular CQ mass accumulation prediction.

Background

Polyphenols, a group of complex naturally occurring compounds, are widely distributed throughout the plant kingdom and are therefore readily consumed by humans. The relationship between their chemical structure and intestinal absorption, transport, and first-pass metabolism remains unresolved, however.

Methods

Here, we investigated the intestinal absorption and first-pass metabolism of four polyphenol compounds, apigenin, resveratrol, emodin and chrysophanol, using the in vitro Caco-2 cell monolayer model system and in situ intestinal perfusion and in vivo pharmacokinetic studies in rats, so as to better understand the relationship between the chemical structure and biological fate of the dietary polyphenols.

Conclusion

After oral administration, emodin and chrysophanol exhibited different absorptive and metabolic behaviours compared to apigenin and resveratrol. The differences in their chemical structures presumably resulted in differing affinities for drug-metabolizing enzymes, such as glucuronidase and sulphatase, and transporters, such as MRP2, SGLT1, and P-glycoprotein, which are found in intestinal epithelial cells.

The purpose of this study was to examine absorption of basic drugs as a function of the composite solubility curve and intestinally relevant pH by using a gastrointestinal tract (GIT) absorption simulation based on the advanced compartmental absorption and transit model. Absorption simulations were carried out for virtual monobasic drugs having a range of pKa, log D, and dose values as a function of presumed solubility and permeability. Results were normally expressed as the combination that resulted in 25% absorption. Absorption of basic drugs was found to be a function of the whole solubility/pH relationship rather than a single solubility value at pH 7. In addition, the parameter spaces of greatest sensitivity were identified. We compared 3 theoretical scenarios: the GIT pH range overlapping (1) only the salt solubility curve, (2) the salt and base solubility curves, or (3) only the base curve. Experimental solubilities of 32 compounds were determined at pHs of 2.2 and 7.4, and they nearly all fitted into 2 of the postulated scenarios. Typically, base solubilities can be simulated in silico, but salt solubilities at low pH can only be measured. We concluded that quality absorption simulations of candidate drugs in most cases require experimental solubility determination at 2 pHs, to permit calculation of the whole solubility/pH profile.

The goal of this study was to apply gastrointestinal simulation technology and integration of physiological parameters to predict biopharmaceutical drug classification. GastroPlus® was used with experimentally determined physicochemical and pharmacokinetic drug properties to simulate the absorption of several weak acid and weak base BCS class II compounds. Simulation of oral drug absorption given physicochemical drug properties and physicochemical parameters will aid justification of biowaivers for selected BCS class II compounds.

Synthetic combinatorial methods now make it practical to readily produce hundreds of thousands of individual compounds, but it is clearly impractical to screen each separately in vivo. We theorized that the direct in vivo testing of mixture-based combinatorial libraries during the discovery phase would enable the identification of novel individual compounds with desirable antinociceptive profiles while simultaneously eliminating many compounds with poor absorption, distribution, metabolism, or pharmacokinetic properties. The TPI 1346 small-molecule combinatorial library is grouped in 120 mixtures derived from 26 functionalities at the first three positions and 42 functionalities at the fourth position of a pyrrolidine bis-cyclic guanidine core scaffold, totaling 738,192 compounds. These 120 mixtures were screened in vivo using the mouse 55°C warm water tail-withdrawal assay to identify mixtures producing antinociception. From these data, two fully defined individual compounds (TPI 1818-101 and TPI 1818-109) were synthesized. These were examined for antinociceptive, respiratory, locomotor, and conditioned place preference effects. The tail-withdrawal assay consistently demonstrated distinctly active mixtures with analgesic activity that was blocked by pretreatment with the non-selective opioid antagonist, naloxone. Based on these results, synthesis and testing of TPI 1818-101 and 1818-109 demonstrated a dose-dependent antinociceptive effect three to five times greater than morphine that was antagonized by mu- or mu- and kappa-opioid receptor selective antagonists, respectively. Neither 1818-101 nor 1818-109 produced significant respiratory depression, hyperlocomotion, or conditioned place preference. Large, highly diverse mixture-based libraries can be screened directly in vivo to identify individual compounds, potentially accelerating the development of promising therapeutics.

Cell-based molecular transport simulations are being developed to facilitate exploratory cheminformatic analysis of virtual libraries of small drug-like molecules. For this purpose, mathematical models of single cells are built from equations capturing the transport of small molecules across membranes. In turn, physicochemical properties of small molecules can be used as input to simulate intracellular drug distribution, through time. Here, with mathematical equations and biological parameters adjusted so as to mimic a leukocyte in the blood, simulations were performed to analyze steady state, relative accumulation of small molecules in lysosomes, mitochondria, and cytosol of this target cell, in the presence of a homogenous extracellular drug concentration. Similarly, with equations and parameters set to mimic an intestinal epithelial cell, simulations were also performed to analyze steady state, relative distribution and transcellular permeability in this non-target cell, in the presence of an apical-to-basolateral concentration gradient. With a test set of ninety-nine monobasic amines gathered from the scientific literature, simulation results helped analyze relationships between the chemical diversity of these molecules and their intracellular distributions.

Electronic supplementary material

The online version of this article (doi:10.1007/s10822-008-9194-7) contains supplementary material, which is available to authorized users.

Adequate bioavailability is one of the essential properties for an orally administered drug. Lipinski and others have formulated simplified rules in which compounds that satisfy selected physiochemical properties, for example, molecular weight (MW)≤500, or logarithm of octanol-water partition coefficient logP(o/w)<5, are anticipated to likely have pharmacokinetic properties appropriate for oral administration. However, these schemes do not simultaneously consider the combination of the physiochemical properties, complicating their application in a more automated fashion. To overcome this we present a novel method to select compounds with a combination of physicochemical properties that maximize bioavailability and druglikeness, based on compounds in the World Drug Index (WDI) database. In the study four properties, MW, logP(o/w), number of hydrogen bond donors and number of hydrogen acceptors were combined into a 4-D histogram, from which a scoring function was defined based on a 4D dependent multivariate Gaussian model. The resulting equation allows for assigning compounds a bioavailability score, termed 4D-BA, such that chemicals with higher 4D-BA scores are more likely to have oral drug-like characteristics. The descriptor is validated by applying the function to drugs previously categorized in the Biopharmaceutics Classification System and examples of application of the descriptor are given in the context of previously published studies targeting heme oxygenase and SHP2 phosphatase. The approach is anticipated to be useful in early lead identification studies in combination with clustering methods to maximize chemical and structural diversity when selecting compounds for biological assays from large database screens. It may also be applied to prioritize synthetically feasible chemical modifications during lead compound optimization.

In silico screening based on the structures of the ligands or of the receptors has become an essential tool to facilitate the drug discovery process but compound collections are needed to carry out such in silico experiments. It has been recognized that absorption, distribution, metabolism, excretion and toxicity (ADME/tox) are key properties that need to be considered early on, even during the database preparation stage. FAF-Drugs is an online service based on Frowns (a chemoinformatics toolkit) that allows users to process their own compound collections via simple ADME/Tox filtering rules such as molecular weight, polar surface area, logP or number of rotatable bonds. SMILES (Simplified Molecular Input Line Entry System), CANSMILES (canonical smiles) or SDF (structure data file) files are required as input and molecules that pass or do not pass the filters are sent back in CANSMILES format. This service should thus help scientists engaging in drug discovery campaigns. Other utilities and several compound collections suitable for in silico screening are available at our site. FAF-Drugs can be accessed at .

Kinetic solubility measurements using prototypical assay buffer conditions are presented for a ~58,000 member library of small molecules. Analyses of the data based upon physical and calculated properties of each individual molecule were performed and resulting trends were considered in the context of commonly held opinions of how physicochemical properties influence aqueous solubility. We further analyze the data using a decision tree model for solubility prediction and via a multi-dimensional assessment of physicochemical relationships to solubility in the context of specific ‘rule-breakers’ relative to common dogma. The role of solubility as a determinant of assay outcome is also considered based upon each compound’s cross-assay activity score for a collection of publicly available screening results. Further, the role of solubility as a governing factor for colloidal aggregation formation within a specified assay setting is examined and considered as a possible cause of a high cross-assay activity score. The results of this solubility profile should aid chemists during library design and optimization efforts and represents a useful training set for computational solubility prediction.

Nutrient absorption in the small intestine cannot occur until molecules are presented to the epithelial cells that line intestinal villi, finger-like protrusions under enteric control. Using a two-dimensional multiscale lattice Boltzmann model of a lid-driven cavity flow with ‘villi’ at the lower surface, we analyse the hypothesis that muscle-induced oscillatory motions of the villi generate a controlled ‘micro-mixing layer’ (MML) that couples with the macro-scale flow to enhance absorption. Nutrient molecules are modelled as passive scalar concentrations at high Schmidt number. Molecular concentration supplied at the cavity lid is advected to the lower surface by a lid-driven macro-scale eddy. We find that micro-scale eddying motions enhance the macro-scale advective flux by creating an MML that couples with the macro-scale flow to increase absorption rate. We show that the MML is modulated by its interactions with the outer flow through a diffusion-dominated layer that separates advection-dominated macro-scale and micro-scale mixed layers. The structure and strength of the MML is sensitive to villus length and oscillation frequency. Our model suggests that the classical explanation for the existence of villi—increased absorptive surface area—is probably incorrect. The model provides support for the potential importance of villus motility in the absorptive function of the small intestine.

Background

Poor oral bioavailability is an important parameter accounting for the failure of the drug candidates. Approximately, 50% of developing drugs fail because of unfavorable oral bioavailability. In silico prediction of oral bioavailability (%F) based on physiochemical properties are highly needed. Although many computational models have been developed to predict oral bioavailability, their accuracy remains low with a significant number of false positives. In this study, we present an oral bioavailability model based on systems biological approach, using a machine learning algorithm coupled with an optimal discriminative set of physiochemical properties.

Results

The models were developed based on computationally derived 247 physicochemical descriptors from 2279 molecules, among which 969, 605 and 705 molecules were corresponds to oral bioavailability, intestinal absorption (HIA) and caco-2 permeability data set, respectively. The partial least squares discriminate analysis showed 49 descriptors of HIA and 50 descriptors of caco-2 are the major contributing descriptors in classifying into groups. Of these descriptors, 47 descriptors were commonly associated to HIA and caco-2, which suggests to play a vital role in classifying oral bioavailability. To determine the best machine learning algorithm, 21 classifiers were compared using a bioavailability data set of 969 molecules with 47 descriptors. Each molecule in the data set was represented by a set of 47 physiochemical properties with the functional relevance labeled as (+bioavailability/−bioavailability) to indicate good-bioavailability/poor-bioavailability molecules. The best-performing algorithm was the logistic algorithm. The correlation based feature selection (CFS) algorithm was implemented, which confirms that these 47 descriptors are the fundamental descriptors for oral bioavailability prediction.

Conclusion

The logistic algorithm with 47 selected descriptors correctly predicted the oral bioavailability, with a predictive accuracy of more than 71%. Overall, the method captures the fundamental molecular descriptors, that can be used as an entity to facilitate prediction of oral bioavailability.

Summary

NOV-002 is a glutathione disulfide (GSSG) mimetic that is in Phase III clinical trials for the treatment of advanced non-small cell lung cancer and other oncology indications. GSSG is reduced by glutathione reductase (GR) to form glutathione (GSH), thereby maintaining redox homeostasis. The purpose of the study was to report the pharmacokinetic properties of NOV-002 and evaluate the effect that NOV-002 elicits in redox homeostasis. The pharmacokinetic analysis and tissue distribution of NOV-002 and GSH was evaluated in mice following a dose of 250 mg/kg, i.p. The redox potential and total protein thiol status was calculated. Here we show that NOV-002 is a substrate for GR and that GSH is a primary metabolite. Nonlinear pharmacokinetic modeling predicted that the estimated absorption and elimination rate constants correspond to a half-life of ~13 mins with an AUC of 1.18 μg.h/ml, a Cmax of 2.16 μg/ml and a volume of distribution of 42.61 L/kg. In addition, measurement of the redox potential and total protein thiol status indicated the generation of a transient oxidative signal in the plasma compartment after administration of NOV-002. These results indicate that NOV-002 exerts kinetic and dynamic effects in mice consistent with the GSSG component as the active pharmacological constituent of the drug. A longer-lasting decrease in total plasma free thiol content was also seen, suggesting that the oxidative effect of the GSSG from NOV-002 was impacting redox homeostasis.

Background

Drug discovery and chemical biology are exceedingly complex and demanding enterprises. In recent years there are been increasing awareness about the importance of predicting/optimizing the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small chemical compounds along the search process rather than at the final stages. Fast methods for evaluating ADMET properties of small molecules often involve applying a set of simple empirical rules (educated guesses) and as such, compound collections' property profiling can be performed in silico. Clearly, these rules cannot assess the full complexity of the human body but can provide valuable information and assist decision-making.

Results

This paper presents FAF-Drugs2, a free adaptable tool for ADMET filtering of electronic compound collections. FAF-Drugs2 is a command line utility program (e.g., written in Python) based on the open source chemistry toolkit OpenBabel, which performs various physicochemical calculations, identifies key functional groups, some toxic and unstable molecules/functional groups. In addition to filtered collections, FAF-Drugs2 can provide, via Gnuplot, several distribution diagrams of major physicochemical properties of the screened compound libraries.

Conclusion

We have developed FAF-Drugs2 to facilitate compound collection preparation, prior to (or after) experimental screening or virtual screening computations. Users can select to apply various filtering thresholds and add rules as needed for a given project. As it stands, FAF-Drugs2 implements numerous filtering rules (23 physicochemical rules and 204 substructure searching rules) that can be easily tuned.

The purpose of this study was to develop simulation and modeling methods for the evaluation of pharmacokinetics when intestinal influx and efflux transporters are involved in gastrointestinal absorption. The advanced compartmental absorption and transit (ACAT) model as part of the computer program GastroPlus™ was used to simulate the absorption and pharmacokinetics of valacyclovir, gabapentin, and talinolol. Each of these drugs is a substrate for an influx or efflux transporter and all show nonlinear dose dependence within the normal therapeutic range. These simulations incorporated the experimentally derived gastrointestinal distributions of transporter expression levels for oligopeptide transporters PepT1 and HPT1 (valacyclovir); System L-amino acid transporter LAT2 and organic cation transporter OCTN1 (gabapentin); and organic anion transporter (OATP1A2) and P-glycoprotein (talinolol). By assuming a uniform distribution of oligopeptide transporter and by application of the in vitro Km value for valacyclovir, the simulations accurately reproduced the experimental nonlinear dose dependence. For gabapentin, LAT2 distribution produced simulation results that were much more accurate than OCTN1 distributions. For talinolol, an influx transporter distribution for OATP1A2 and the efflux transporter P-glycoprotein distributed with increasing expression in the distal small intestine produced the best results. The physiological characteristics of the small and large intestines used in the ACAT model were able to accurately account for the positional and temporal changes in concentration and carrier-mediated transport of the three drugs included in this study. The ACAT model reproduced the nonlinear dose dependence for each of these drugs.

Accurate predictions of human pharmacokinetic and pharmacodynamic (PK/PD) profiles are critical in early drug development, as safe, efficacious, and “developable” dosing regimens of promising compounds have to be identified. While advantages of successful integration of preclinical PK/PD data in the “anticipation” of human doses (AHD) have been recognized, pharmaceutical scientists have faced difficulties with practical implementation, especially for PK/PD profile projections of compounds with challenging absorption, distribution, metabolism, excretion and formulation properties. In this article, practical projection approaches for formulation-dependent human PK/PD parameters and profiles of Biopharmaceutics Classification System classes I-IV drugs based on preclinical data are described. Case examples for “AHD” demonstrate the utility of preclinical and clinical PK/PD modeling for formulation risk identification, lead candidate differentiation, and prediction of clinical outcome. The application of allometric scaling methods and physiologically based pharmacokinetic approaches for clearance or volume of distribution projections is described using GastroPlus™. Methods to enhance prediction confidence such as in vitro–in vivo extrapolations in clearance predictions using in vitro microsomal data are discussed. Examples for integration of clinical PK/PD and formulation data from frontrunner compounds via “reverse pharmacology strategies” that minimize uncertainty with PK/PD predictions are included. The use of integrated softwares such as GastroPlus™ in combination with established PK projection methods allow the projection of formulation-dependent preclinical and human PK/PD profiles required for compound differentiation and development risk assessments.

Sequence-derived structural and physicochemical features have frequently been used in the development of statistical learning models for predicting proteins and peptides of different structural, functional and interaction profiles. PROFEAT (Protein Features) is a web server for computing commonly-used structural and physicochemical features of proteins and peptides from amino acid sequence. It computes six feature groups composed of ten features that include 51 descriptors and 1447 descriptor values. The computed features include amino acid composition, dipeptide composition, normalized Moreau–Broto autocorrelation, Moran autocorrelation, Geary autocorrelation, sequence-order-coupling number, quasi-sequence-order descriptors and the composition, transition and distribution of various structural and physicochemical properties. In addition, it can also compute previous autocorrelations descriptors based on user-defined properties. Our computational algorithms were extensively tested and the computed protein features have been used in a number of published works for predicting proteins of functional classes, protein–protein interactions and MHC-binding peptides. PROFEAT is accessible at

The origins and advancements of pharmacy, medicinal chemistry, and drug discovery are interwoven in nature. Medicinal chemistry provides pharmacy students with a thorough understanding of drug mechanisms of action, structure-activity relationships (SAR), acid-base and physicochemical properties, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles. A comprehensive understanding of the chemical basis of drug action equips pharmacy students with the ability to answer rationally the “why” and “how” questions related to drug action and it sets the pharmacist apart as the chemical expert among health care professionals. By imparting an exclusive knowledge base, medicinal chemistry plays a vital role in providing critical thinking and evidence-based problem-solving skills to pharmacy students, enabling them to make optimal patient-specific therapeutic decisions. This review highlights the parallel nature of the history of pharmacy and medicinal chemistry, as well as the key elements of medicinal chemistry and drug discovery that make it an indispensable component of the pharmacy curriculum.

Pharmacogenomics is defined as the study of the impacts of heritable traits on pharmacology and toxicology. Candidate genes with potential pharmacogenomic importance include drug transporters involved in absorption and excretion, phase I enzymes (e.g., cytochrome P450-dependent mixed-function oxidases) and phase II enzymes (e.g., glucuronosyltransferases) contributing to metabolism, and those molecules (e.g., albumin, A1-acid glycoprotein, and lipoproteins) involved in the distribution of antifungal compounds. By using the tools of population genetics to define interindividual differences in drug absorption, distribution, metabolism, and excretion, pharmacogenomic models for genetic variations in antifungal pharmacokinetics can be derived. Pharmacogenomic factors may become especially important in the treatment of immunocompromised patients or those with persistent or refractory mycoses that cannot be explained by elevated MICs and where rational dosage optimization of the antifungal agent may be particularly critical. Pharmacogenomics has the potential to shift the paradigm of therapy and to improve the selection of antifungal compounds and adjustment of dosage based upon individual variations in drug absorption, metabolism, and excretion.

A significant number of drug discovery efforts are based on natural products or high throughput screens from which compounds showing potential therapeutic effects are identified without knowledge of the target molecule or its 3D structure. In such cases computational ligand-based drug design (LBDD) can accelerate the drug discovery processes. LBDD is a general approach to elucidate the relationship of a compound's structure and physicochemical attributes to its biological activity. The resulting structure-activity relationship (SAR) may then act as the basis for the prediction of compounds with improved biological attributes. LBDD methods range from pharmacophore models identifying essential features of ligands responsible for their activity, quantitative structure-activity relationships (QSAR) yielding quantitative estimates of activities based on physiochemical properties, and to similarity searching, which explores compounds with similar properties as well as various combinations of the above. A number of recent LBDD approaches involve the use of multiple conformations of the ligands being studied. One of the basic components to generate multiple conformations in LBDD is molecular mechanics (MM), which apply an empirical energy function to relate conformation to energies and forces. The collection of conformations for ligands is then combined with functional data using methods ranging from regression analysis to neural networks, from which the SAR is determined. Accordingly, for effective application of LBDD for SAR determinations it is important that the compounds be accurately modelled such that the appropriate range of conformations accessible to the ligands is identified. Such accurate modelling is largely based on use of the appropriate empirical force field for the molecules being investigated and the approaches used to generate the conformations. The present chapter includes a brief overview of currently used SAR methods in LBDD followed by a more detailed presentation of issues and limitations associated with empirical energy functions and conformational sampling methods.

Background

The current approach to screen for drug-like molecules is to sieve for molecules with biochemical properties suitable for desirable pharmacokinetics and reduced toxicity, using predominantly biophysical properties of chemical compounds, based on empirical rules such as Lipinski's "rule of five" (Ro5). For over a decade, Ro5 has been applied to combinatorial compounds, drugs and ligands, in the search for suitable lead compounds. Unfortunately, till date, a clear distinction between drugs and non-drugs has not been achieved. The current trend is to seek out drugs which show metabolite-likeness. In identifying similar physicochemical characteristics, compounds have usually been clustered based on some characteristic, to reduce the search space presented by large molecular datasets. This paper examines the similarity of current drug molecules with human metabolites and toxins, using a range of computed molecular descriptors as well as the effect of comparison to clustered data compared to searches against complete datasets.

Results

We have carried out statistical and substructure functional group analyses of three datasets, namely human metabolites, drugs and toxin molecules. The distributions of various molecular descriptors were investigated. Our analyses show that, although the three groups are distinct, present-day drugs are closer to toxin molecules than to metabolites. Furthermore, these distributions are quite similar for both clustered data as well as complete or unclustered datasets.

Conclusion

The property space occupied by metabolites is dissimilar to that of drugs or toxin molecules, with current drugs showing greater similarity to toxins than to metabolites. Additionally, empirical rules like Ro5 can be refined to identify drugs or drug-like molecules that are clearly distinct from toxic compounds and more metabolite-like. The inclusion of human metabolites in this study provides a deeper insight into metabolite/drug/toxin-like properties and will also prove to be valuable in the prediction or optimization of small molecules as ligands for therapeutic applications.

The utility of model repositories is discussed in the context of systems biology (SB). It is shown how such repositories, and in particular their live versions, can be used for computational SB: we calculate the robustness of the yeast glycolytic network with respect to perturbations of one of its enzyme activities and one transport system. The robustness with respect to perturbations in the key enzyme phosphofructokinase is surprisingly large. We then note the upcoming convergence of pharmacokinetics–pharmacodynamics (PK–PD) and bottom-up SB. In PK alone, quite a few one-, two- or more compartment models provide valuable initial guesses and insights into the absorption, distribution, metabolism and excretion of particular drugs. These models are phenomenological however, forbidding implementation of molecule-based tools and network information. In order to facilitate a fruitful synergy between SB and PK–PD, and between PK and PD, we present a new platform that combines standard phenomenological models used in the PK/PD field with mechanism-based SB models and approaches.