Prediction of pharmacokinetic properties using experimental approaches during early drug discovery

Insilico studies of organosulfur-functional active compounds in garlic

Garlic has been used medicinally since antiquity because of its antimicrobial activity, anticancer activity, antioxidant activity, ability to reduce cardiovascular diseases, improving immune functions, and antidiabetic activities and also in reducing cardiovascular diseases and improving immune functions. Recent studies identify that the wide variety of medicinal functions are attributed to the sulfur compounds present in garlic. Epidemiological observations and laboratory studies in animal models have also showed anticarcinogenic potential of organosulfur compounds of garlic. In this study, in silico analysis of organosulfur compounds is reported using the methods of theoretical chemistry to elucidate the molecular properties of garlic as it is more time and cost efficient, reduces the number of wet experiments, and offers the possibility of replacing some animal tests with suitable in silico models. The analysis of molecular descriptors defined by Lipinski has been done. The solubility of drug in water has been determined as it is of useful importance in the process of drug discovery from molecular design to pharmaceutical formulation and biopharmacy. All toxicities associated with candidate drug have been calculated. P-Glycoprotein expressed in normal tissues as a cause of drug pharmacokinetics and pharmacodynamics has been examined. Drug-plasma protein binding and volume of distribution have also been calculated. To avoid rejection of drugs, it is becoming more important to determine pK(a), absorption, polar surface area, and other physiochemical properties associated with a drug, before synthetic work is undertaken. The present in silico study is aimed at examining these compounds of garlic to evaluate its possible efficacy and toxicity under conditions of actual use in humans.

Use of intrinsic clearance for prediction of human hepatic clearance

IMPORTANCE OF THE FIELD

The use of intrinsic metabolic stability/clearance and other in vitro pharmacokinetic data for the selection of drug candidates for clinical evaluation during discovery lead optimization has become one of the primary focuses of research organizations involved in new drug discovery. Using intrinsic clearance determined from human liver microsomal preparations and/or hepatocyte to predict human clearance has become more acceptable.

AREAS COVERED IN THIS REVIEW

This review focuses on the current methods for determining intrinsic clearance and scaling to predict human hepatic clearance, and novel physiologically-based models for improvement of human hepatic clearance prediction. Published microsomal metabolic stability data and in-house hepatocyte clearance data were compared with published in vivo human hepatic clearance data. Various scaling models and the effect of protein binding were examined.

WHAT THE READER WILL GAIN

Use of a novel microfluidic model and other physiologically-based models are presented. Microsomal metabolic clearance requires correction for protein binding and in vitro microsomal binding in order to better predict in vivo hepatic clearance of compounds that are mainly eliminated by hepatic metabolism.

TAKE HOME MESSAGE

Metabolic clearance obtained using hepatocytes may work well in combination with the well-stirred model. Novel models incorporating flow and protein binding in the system may be the most complete models for prediction of human in vivo metabolism.

Estimation of ADME Properties in Drug Discovery: Predicting Caco-2 Cell Permeability Using Atom-Based Stochastic and Non-stochastic Linear Indices

The in vitro determination of the permeability through cultured Caco-2 cells is the most often-used in vitro model for drug absorption. In this report, we use the largest data set of measured P(Caco-2), consisting of 157 structurally diverse compounds. Linear discriminant analysis (LDA) was used to obtain quantitative models that discriminate higher absorption compounds from those with moderate-poorer absorption. The best LDA model has an accuracy of 90.58% and 84.21% for training and test set. The percentage of good correlation, in the virtual screening of 241 drugs with the reported values of the percentage of human intestinal absorption (HIA), was greater than 81%. In addition, multiple linear regression models were developed to predict Caco-2 permeability with determination coefficients of 0.71 and 0.72. Our method compares favorably with other approaches implemented in the Dragon software, as well as other methods from the international literature. These results suggest that the proposed method is a good tool for studying the oral absorption of drug candidates.

Lead Optimization in Discovery Drug Metabolism and Pharmacokinetics/Case study: The Hepatitis C Virus (HCV) Protease Inhibitor SCH 503034

Lead optimization using drug metabolism and pharmacokinetics (DMPK) parameters has become one of the primary focuses of research organizations involved in drug discovery in the last decade. Using a combination of rapid in vivo and in vitro DMPK screening procedures on a large array of compounds during the lead optimization process has resulted in development of compounds that have acceptable DMPK properties. In this review, we present a general screening paradigm that is currently being used as part of drug discovery at Schering-Plough and we describe a case study using the Hepatitis C Virus (HCV) protease inhibitor program as an example. By using the DMPK optimization tools, a potent HCV protease inhibitor, SCH 503034, was selected for development as a candidate drug.

Convenient Nonlinear Model for Predicting the Tissue/Blood Partition Coefficients of Seven Human Tissues of Neutral, Acidic, and Basic Structurally Diverse Compounds

In this work, the tissue/blood partition coefficients of seven human tissues were calculated using a nonlinear regression analysis. The dataset contained 80 structurally diverse compounds distributing into the brain, kidney, muscle, lung, liver, heart, and fat, whose acidic and basic properties were also considered by introducing the three possible forms of the compound in the human body (neutral, cationic, and anionic forms). A total of 248 data points were there in the training set (eq 5: r = 0.877, s = 0.352; eq 6: r = 0.869, s = 0.362) and 49 data points in the testing set (eq 5: r = 0.844, s = 0.342; eq 6: r = 0.860, s = 0.311). It was also concluded that the same state (neutral, cation, and anion) of a compound has essentially identical partition coefficients between the same tissue composition and the blood in these tissues. Only the different content of the three tissue compositions (lipid, protein, and water) lead to the different partition coefficient in different tissues, which offered a significant conclusion for the drug's distribution research.

High throughput microsomal stability assay for insoluble compounds

High throughput metabolic stability assays are widely implemented in drug discovery to guide structural modification, predict in vivo performance, develop structure-metabolic stability relationships, and triage compounds for in vivo animal studies. However, these methods are often developed and validated using commercial drugs. Many drug discovery compounds differ from commercial drugs, with many having high lipophilicity, high molecular weight and low solubility. The impact of very low solubility on metabolic stability assay results was explored. Two metabolic stability assays, the 'aqueous dilution method' and the 'cosolvent method, were compared. For commercial drugs and most discovery compounds having reasonable drug-like properties, the two methods gave comparable results. For highly lipophilic, insoluble drug discovery compounds, the 'aqueous dilution method' gave artificially higher stability results. The cosolvent method performs compound dilutions in solutions with higher organic solvent content and adds solutions directly to microsomes to assist with solubilization, minimize precipitation and reduce non-specific binding to plastics. This method is more applicable in drug discovery where compounds of a wide range of solubility are studied.

In silico ADME modelling 2: Computational models to predict human serum albumin binding affinity using ant colony systems

Modelling of in vitro human serum albumin (HSA) binding data of 94 diverse drugs and drug-like compounds is performed to develop global predictive models that are applicable to the whole medicinal chemistry space. For this aim, ant colony systems, a stochastic method along with multiple linear regression (MLR), is employed to exhaustively search and select multivariate linear equations, from a pool of 327 molecular descriptors. This methodology helped us to derive optimal quantitative structure-property relationship (QSPR) models based on five and six descriptors with excellent predictive power. The best five-descriptor model is based on Kier and Hall valence connectivity index--Order 5 (path), Auto-correlation descriptor (Broto-Moreau) weighted by atomic masses--Order 4, Auto-correlation descriptor (Broto-Moreau) weighted by atomic polarizabilities--Order 5, AlogP98, SklogS (calculated buffer water solubility) [R=0.8942, Q=0.86790, F=62.24 and SE=0.2626]; the best six-variable model is based on Kier and Hall valence connectivity index of Order 3 (cluster), Auto-correlation descriptor (Broto-Moreau) weighted by atomic masses--Order 4, Auto-correlation descriptor (Broto-Moreau) weighted by atomic polarizabilities--Order 5, Atomic-Level-Based AI topological descriptors--AIdsCH, AlogP98, SklogS (calculated buffer water solubility) [R=0.9128, Q=0.89220, F=64.09 and SE=0.2411]. From the analysis of the physical meaning of the selected descriptors, it is inferred that the binding affinity of small organic compounds to human serum albumin is principally dependent on the following fundamental properties: (1) hydrophobic interactions, (2) solubility, (3) size and (4) shape. Finally, as the models reported herein are based on computed properties, they appear to be a valuable tool in virtual screening, where selection and prioritisation of candidates is required.

Multi-target strategies for the improved treatment of depressive states: Conceptual foundations and neuronal substrates, drug discovery and therapeutic application

Major depression is a debilitating and recurrent disorder with a substantial lifetime risk and a high social cost. Depressed patients generally display co-morbid symptoms, and depression frequently accompanies other serious disorders. Currently available drugs display limited efficacy and a pronounced delay to onset of action, and all provoke distressing side effects. Cloning of the human genome has fuelled expectations that symptomatic treatment may soon become more rapid and effective, and that depressive states may ultimately be "prevented" or "cured". In pursuing these objectives, in particular for genome-derived, non-monoaminergic targets, "specificity" of drug actions is often emphasized. That is, priority is afforded to agents that interact exclusively with a single site hypothesized as critically involved in the pathogenesis and/or control of depression. Certain highly selective drugs may prove effective, and they remain indispensable in the experimental (and clinical) evaluation of the significance of novel mechanisms. However, by analogy to other multifactorial disorders, "multi-target" agents may be better adapted to the improved treatment of depressive states. Support for this contention is garnered from a broad palette of observations, ranging from mechanisms of action of adjunctive drug combinations and electroconvulsive therapy to "network theory" analysis of the etiology and management of depressive states. The review also outlines opportunities to be exploited, and challenges to be addressed, in the discovery and characterization of drugs recognizing multiple targets. Finally, a diversity of multi-target strategies is proposed for the more efficacious and rapid control of core and co-morbid symptoms of depression, together with improved tolerance relative to currently available agents.

Strategies to improve plasma half life time of peptide and protein drugs

Due to the obvious advantages of long-acting peptide and protein drugs, strategies to prolong plasma half life time of such compounds are highly on demand. Short plasma half life times are commonly due to fast renal clearance as well as to enzymatic degradation occurring during systemic circulation. Modifications of the peptide/protein can lead to prolonged plasma half life times. By shortening the overall amino acid amount of somatostatin and replacing L: -analogue amino acids with D: -amino acids, plasma half life time of the derivate octreotide was 1.5 hours in comparison to only few minutes of somatostatin. A PEG(2,40 K) conjugate of INF-alpha-2b exhibited a 330-fold prolonged plasma half life time compared to the native protein. It was the aim of this review to provide an overview of possible strategies to prolong plasma half life time such as modification of N- and C-terminus or PEGylation as well as methods to evaluate the effectiveness of drug modifications. Furthermore, fundamental data about most important proteolytic enzymes of human blood, liver and kidney as well as their cleavage specificity and inhibitors for them are provided in order to predict enzymatic cleavage of peptide and protein drugs during systemic circulation.

Measurement of drug–protein binding by immobilized human serum albumin-HPLC and comparison with ultrafiltration

An HPLC method employing CHIRAL-I (150 mm x 3 mm), 5 microm column from Chrom. Tech., immobilized with human serum albumin (HSA), was used to determine in vitro protein binding of several compounds. Experimentally obtained plasma protein data exhibited good correlation with the reported values. The method was compared with the conventional ultra filtration technique and both yielded similar results. Proprietary compounds that could not be analyzed by ultra filtration due to high non-specific binding to filter membrane were successfully analyzed by HSA-HPLC method. On the other hand, two proprietary compounds did not elute from HSA column due to strong binding, but were successfully analyzed by ultra filtration. This proves that both the techniques have their own merits and demerits and should be exploited judiciously as per the requirement. The plasma protein binding studies conducted on four gyrase inhibitors in rat and human plasma exhibited no interspecies difference via ultra filtration method. Further, it was also observed that the protein binding obtained for the four gyrase inhibitors by HSA-HPLC method was not only similar to that obtained by ultra filtration in human plasma but was also in accordance with ex vivo and in vitro protein binding obtained for rat plasma after ultra filtration because these compounds predominantly bind to HSA The binding of several compounds to alpha1-acid glycoprotein (AGP), another important plasma protein, was also examined using AGP immobilized column. However, the data could not be relied upon since some anti-bacterials and non-steroidal anti-inflammatory drugs (NSAIDS), which are known to predominantly bind to HSA, were also found to bind to AGP.

Antibacterial peptides and proteins with multiple cellular targets

Native antimicrobial peptides and proteins represent bridges between innate and adaptive immunity in mammals. On the one hand they possess direct bacterial killing properties, partly by disintegrating bacterial membranes, and some also by inhibiting functions of intracellular biopolymers. On the other, native antimicrobial peptides and proteins upregulate the host defense as chemoattractants or by various additional immunostimulatory effects. Structure-activity relationship studies indicate that residues responsible for the activities on bacterial membranes or for the secondary functions do not perfectly overlap. In reality, in spite of the relatively short size (18-20 amino acid residues) of some of these molecules, the functional domains can frequently be separated, with the cell-penetrating fragments located at the C-termini and the protein binding domains found upstream. As a cumulative effect, multifunctional and target-specific (agonist or antagonist) antimicrobial peptides and proteins interfere with more than one bacterial function at low concentrations, eliminating toxicity concerns of the earlier generations of antibacterial peptides observed in the clinical setting.

Ultra-performance liquid chromatography coupled to time-of-flight mass spectrometry for robust, high-throughput quantitative analysis of an automated metabolic stability assay, with simultaneous determination of metabolic data

The application of ultra-performance liquid chromatography coupled to time-of-flight mass spectrometry (UPLC/TOFMS) for high-throughput analysis of a 96-well plate based metabolic stability assay has been investigated. Full-scan data were acquired, with run times of 2.5-3.5 min, from which narrow window extracted ion chromatograms were generated, producing quantitative data for the test compound equivalent to that obtained by high-performance liquid chromatography with tandem mass spectrometric detection on a triple quadrupole instrument (HPLC/MS/MS). Sensitivity and mass accuracy were maintained over the analysis of >300 samples. Additionally, the UPLC/TOFMS datasets obtained gave access to metabolic route information, at no cost in terms of sensitivity for the test compound.

High throughput P450 inhibition screens in early drug discovery

This review of high throughput (HT) P450 inhibition technologies and their impact on early drug discovery finds the field at a mature stage. The relationship between P450 inhibition and drug-drug interactions is well understood. A wide variety of P450 inhibition detection technologies are readily available off-the-shelf, but what seems still to be missing is a general agreement on how much weight one should give to the various types of early discovery HT P450 inhibition data. Method-dependent potency differences are a cause of concern, and to resolve this issue the authors advocate calibration of the HT methods with a large set of marketed drugs.

In silico ADME modelling: prediction models for blood-brain barrier permeation using a systematic variable selection method

Quantitative Structure-Property Relationship models (QSPR) based on in vivo blood-brain permeation data (logBB) of 88 diverse compounds, 324 descriptors and a systematic variable selection method, namely 'Variable Selection and Modeling method based on the prediction (VSMP)', are reported. Of all the models developed using VSMP, the best three-descriptors model is based on Atomic type E-state index (SsssN), AlogP98 and Van der Waal's surface area (r=0.8425, q=0.8239, F=68.49 and SE=0.4165); the best four-descriptors model is based on Kappa shape index of order 1, Atomic type E-state index (SsssN), Atomic level based AI topological descriptor (AIssssC) and AlogP98 (r=0.8638, q=0.8472, F=60.982 and SE=0.3919). The performance of the models on three test sets taken from the literature is illustrated and compared with the results from other reported computational approaches. Test set III constitutes 91 compounds from the literature with known qualitative BBB indication and is used for virtual screening studies. The success rate of the reported models is 82% in the case of BBB+ compounds and a similar success rate is observed with BBB- compounds. Finally, as the models reported herein are based on computed properties, they appear as a valuable tool in virtual screening, where selection and prioritization of candidates is required.

Molecular determinants of substrate/inhibitor binding to the human and rabbit renal organic cation transporters hOCT2 and rbOCT2

Organic cation transporters are important for the elimination of many drugs and toxins from the body. In the present study, substrate-transporter interactions were investigated in Chinese hamster ovary cells stably transfected with either the human or rabbit orthologs of the principal organic cation transporter in the kidney, OCT2. IC(50) values, ranging from 0.04 muM to >3 mM, for inhibition of [(14)C]tetraethylammonium transport were determined for more than 30 structurally diverse compounds. Although the two OCT orthologs displayed similar IC(50) values for some of these compounds, the majority varied by as much as 20-fold. Marked differences in substrate affinity were also noted when comparing hOCT2 to the closely related homolog hOCT1. These data suggest the molecular determinants of substrate binding differ markedly among both homologous and orthologous OCT transporters. The software package Cerius(2) (Accelrys, San Diego, CA) was used to generate a descriptor-based, two-dimensional, quantitative structure-activity relationship (QSAR) to produce a model relating the affinity of hOCT2 to particular physicochemical features of substrate/inhibitor molecules (r(2) = 0.81). Comparative molecular field analysis (Tripos, St. Louis, MO) was used to generate three-dimensional QSARs describing the structural basis of substrate binding to hOCT2 and rbOCT2 (q(2) = 0.60 and 0.53, respectively, and each with r(2) = 0.97). The quality of the models was assessed by their ability to successfully predict the inhibition of a set of test compounds. The current models enabled prediction of OCT2 affinity and may prove useful in the prediction of unwanted drug interactions at the level of the renal secretory process.

ADME Evaluation in Drug Discovery. 3. Modeling Blood-Brain Barrier Partitioning Using Simple Molecular Descriptors

In this paper, QSPR models were developed for in vivo blood-brain partitioning data (logBB) of a large data set consisting of 115 diverse organic compounds. The best model is based on three descriptors: n-octanol/water partition coefficient calculated using the SLOGP approach, logP; high-charged polar surface areas based on the Gasteiger partial charges, HCPSA, and the excessive molecular weight larger than 360, MW(360). The model bears good statistical significance, n = 78, r = 0.88, q = 0.86, s = 0.36, F = 81.5. The actual prediction potential of the model was validated through two external validation sets of 37 diverse compounds. The predicted results demonstrate that the model bears better prediction potential than many other models and can be used for logBB estimations for drug and drug-like molecules. Comparison of several logP calculation approaches suggests that logP calculated by SLOGP can be used as a significant descriptor for the prediction of molecular transport properties because SLOGP gives the most similar results with CLOGP. The QSPR model indicates that larger polar surface areas have a more negative contribution to logBB, but the absolute partial charges on the atoms surrounded by the polar surfaces should be larger than 0.10|e|. Meanwhile, tight junction membranes limit the size of hydrophilic molecules that can cross the membrane with a molecular weight of approximately 360, because when a molecule's weight is larger than 360 it shows a negative contribution to logBB. The computations of molecular surface, partial charges, logP, and logBB have been accomplished using a program called Drug-BB. Moreover, to improve the efficiency of the computations of logP, we made an extensive reparametrization of SLOGP, and the newly developed SLOGP model is only based on simple atomic addition. Further, we developed a set of parameters to calculate the topological polar surface area (TPSA), thus the high-charged topological polar surface area (HCTPSA) could be estimated from the 2D connection information of a molecule. Adopting the new strategies, the estimations of logP, HCTPSA, and logBB are only based on the topological structure of a molecule and therefore, can be used for fast screening of virtual libraries having millions of molecules.

Molecular properties that influence the oral bioavailability of drug candidates

Oral bioavailability measurements in rats for over 1100 drug candidates studied at SmithKline Beecham Pharmaceuticals (now GlaxoSmithKline) have allowed us to analyze the relative importance of molecular properties considered to influence that drug property. Reduced molecular flexibility, as measured by the number of rotatable bonds, and low polar surface area or total hydrogen bond count (sum of donors and acceptors) are found to be important predictors of good oral bioavailability, independent of molecular weight. That on average both the number of rotatable bonds and polar surface area or hydrogen bond count tend to increase with molecular weight may in part explain the success of the molecular weight parameter in predicting oral bioavailability. The commonly applied molecular weight cutoff at 500 does not itself significantly separate compounds with poor oral bioavailability from those with acceptable values in this extensive data set. Our observations suggest that compounds which meet only the two criteria of (1) 10 or fewer rotatable bonds and (2) polar surface area equal to or less than 140 A(2) (or 12 or fewer H-bond donors and acceptors) will have a high probability of good oral bioavailability in the rat. Data sets for the artificial membrane permeation rate and for clearance in the rat were also examined. Reduced polar surface area correlates better with increased permeation rate than does lipophilicity (C log P), and increased rotatable bond count has a negative effect on the permeation rate. A threshold permeation rate is a prerequisite of oral bioavailability. The rotatable bond count does not correlate with the data examined here for the in vivo clearance rate in the rat.