Prediction of drug bioavailability based on molecular structure

Prediction of the First-Pass Metabolism of a Drug After Oral Intake Based on Structural Parameters and Physicochemical Properties

BACKGROUND AND OBJECTIVE

The oral first-pass metabolism is a crucial factor that plays a key role in a drug's pharmacokinetic profile. Prediction of the oral first-pass metabolism based on chemical structural parameters can be useful in the drug-design process. Developing an orally administered drug with an acceptable pharmacokinetic profile is necessary to reduce the cost and time associated with evaluating the extent of the first-pass metabolism of a candidate compound in preclinical studies. The aim of this study is to estimate the first-pass metabolism of an orally administered drug.

METHODS

A set of compounds with reported first-pass metabolism data were collected. Moreover, human intestinal absorption percentage and oral bioavailability data were extracted from the literature to propose a classification system that split the drugs up based on their first-pass metabolism extents. Various structural parameters were calculated for each compound. The relations of the structural and physicochemical values of each compound to the class the compound belongs to were obtained using logistic regression.

RESULTS

Initial analysis showed that compounds with logD7.4 > 1 or a rugosity factor of > 1.5 are more likely to have high first-pass metabolism. Four different models that can predict the oral first-pass metabolism with acceptable error were introduced. The overall accuracies of the models were in the range of 72% (for models with simple descriptors) to 78% (for models with complex descriptors). Although the models with simple descriptors have lower accuracies compared to complex models, they are more interpretable and easier for researchers to utilize.

CONCLUSION

A novel classification of drugs based on the extent of the oral first-pass metabolism was introduced, and mechanistic models were developed to assign candidate compounds to the appropriate proposed classes.

Simulation Models for Prediction of Bioavailability of Medicinal Drugs-the Interface Between Experiment and Computation

The oral drug bioavailability (BA) problems have remained inevitable over the years, impairing drug efficacy and indirectly leading to eventual human morbidity and mortality. However, some conventional lab-based methods improve drug absorption leading to enhanced BA, and the recent experimental techniques are up-and-coming. Nevertheless, some have inherent drawbacks in improving the efficacy of poorly insoluble and low impermeable drugs. Drug BA and strategies to overcome these challenges were briefly highlighted. This review has significantly unravelled the different computational models for studying and predicting drug bioavailability. Several computational approaches provide mechanistic insights into the oral drug delivery system simulation of descriptors like solubility, permeability, transport protein-ligand interactions, and molecular structures. The in silico techniques have long been known still are just being applied to unravel drug bioavailability issues. Many publications have reported novel applications of the computational models towards achieving improved drug BA, including predicting gastrointestinal tract (GIT) drug absorption properties and passive intestinal membrane permeability, thus maximizing time and resources. Also, the classical molecular simulation models for free solvation energies of soluble-related processes such as solubilization, dissolutions, supersaturation, and precipitation have been used in virtual screening studies. A few of the tools are GastroPlus^TM that supports biowaiver for drugs, mainly BCS class III and predicts drug compounds' absorption and pharmacokinetic process; SimCyp® simulator for mechanistic modelling and simulation of drug formulation processes; pharmacodynamics analysis on non-linear mixed-effects modelling; and mathematical models, predicting absorption potential/maximum absorption dose. This review provides in silico-experiment annexation in the drug bioavailability enhancement, possible insights that lead to critical opinion on the applications and reliability of the various in silico models as a growing tool for drug development and discovery, thus accelerating drug development processes.

Pharmaceutical Excipients and Drug Metabolism: A Mini-Review

Conclusions from previously reported articles have revealed that many commonly used pharmaceutical excipients, known to be pharmacologically inert, show effects on drug transporters and/or metabolic enzymes. Thus, the pharmacokinetics (absorption, distribution, metabolism and elimination) of active pharmaceutical ingredients are possibly altered because of their transport and metabolism modulation from the incorporated excipients. The aim of this review is to present studies on the interaction of various commonly-used excipients on pre-systemic metabolism by CYP450 enzymes. Excipients such as surfactants, polymers, fatty acids and solvents are discussed. Based on all the reported outcomes, the most potent inhibitors were found to be surfactants and the least effective were organic solvents. However, there are many factors that can influence the inhibition of CYP450, for instance type of excipient, concentration of excipient, type of CYP450 isoenzyme, incubation condition, etc. Such evidence will be very useful in dosage form design, so that the right formulation can be designed to maximize drug bioavailability, especially for poorly bioavailable drugs.

Molecular Modeling Approaches for the Prediction of Selected Pharmacokinetic Properties

Poor profiles of potential drug candidates, including pharmacokinetic properties, have been acknowledged as a significant hindrance to the development of modern therapeutics. Contemporary drug discovery and development would be incomplete without the aid of molecular modeling (in-silico) techniques, allowing the prediction of pharmacokinetic properties such as clearance, unbound fraction, volume of distribution and bioavailability. As with all models, in-silico approaches are subject to their interpretability, a trait that must be balanced with accuracy when considering the development of new methods. The best models will always require reliable data to inform them, presenting significant challenges, particularly when appropriate in-vitro or in-vivo data may be difficult or time-consuming to obtain. This article seeks to review some of the key in-silico techniques used to predict key pharmacokinetic properties and give commentary on the current and future directions of the field.

Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry

Recent transformative advances in computing power and algorithms have made computational chemistry central to the discovery and design of new molecules and materials. First-principles simulations are increasingly accurate and applicable to large systems with the speed needed for high-throughput computational screening. Despite these strides, the combinatorial challenges associated with the vastness of chemical space mean that more than just fast and accurate computational tools are needed for accelerated chemical discovery. In transition-metal chemistry and catalysis, unique challenges arise. The variable spin, oxidation state, and coordination environments favored by elements with well-localized d or f electrons provide great opportunity for tailoring properties in catalytic or functional (e.g., magnetic) materials but also add layers of uncertainty to any design strategy. We outline five key mandates for realizing computationally driven accelerated discovery in inorganic chemistry: (i) fully automated simulation of new compounds, (ii) knowledge of prediction sensitivity or accuracy, (iii) faster-than-fast property prediction methods, (iv) maps for rapid chemical space traversal, and (v) a means to reveal design rules on the kilocompound scale. Through case studies in open-shell transition-metal chemistry, we describe how advances in methodology and software in each of these areas bring about new chemical insights. We conclude with our outlook on the next steps in this process toward realizing fully autonomous discovery in inorganic chemistry using computational chemistry.

Computational modeling of human oral bioavailability: what will be next?

INTRODUCTION

The oral route is the most convenient way of administrating drugs. Therefore, accurate determination of oral bioavailability is paramount during drug discovery and development. Quantitative structure-property relationship (QSPR), rule-of-thumb (RoT) and physiologically based-pharmacokinetic (PBPK) approaches are promising alternatives to the early oral bioavailability prediction. Areas covered: The authors give insight into the factors affecting bioavailability, the fundamental theoretical framework and the practical aspects of computational methods for predicting this property. They also give their perspectives on future computational models for estimating oral bioavailability. Expert opinion: Oral bioavailability is a multi-factorial pharmacokinetic property with its accurate prediction challenging. For RoT and QSPR modeling, the reliability of datasets, the significance of molecular descriptor families and the diversity of chemometric tools used are important factors that define model predictability and interpretability. Likewise, for PBPK modeling the integrity of the pharmacokinetic data, the number of input parameters, the complexity of statistical analysis and the software packages used are relevant factors in bioavailability prediction. Although these approaches have been utilized independently, the tendency to use hybrid QSPR-PBPK approaches together with the exploration of ensemble and deep-learning systems for QSPR modeling of oral bioavailability has opened new avenues for development promising tools for oral bioavailability prediction.

Xylitol as a potential co-crystal co-former for enhancing dissolution rate of felodipine: preparation and evaluation of sublingual tablets

Dissolution enhancement is a promising strategy for improving drug bioavailability. Co-crystallization of drugs with inert material can help in this direction. The benefit will become even greater if the inert material can form co-crystal while maintaining its main function as excipient. Accordingly, the objective of the current study was to investigate xylitol as a potential co-crystal co-former for felodipine with the goal of preparing felodipine sublingual tablets. Co-crystallization was achieved by wet co-grinding of the crystals deposited from methanolic solutions containing felodipine with increasing molar ratios of xylitol (1:1, 1:2 and 1:3). The developed co-crystals were characterized using Fourier transform infrared spectroscopy (FTIR), X-ray diffractometry (XRD), differential scanning calorimetry (DSC) and scanning electron microscopy (SEM) before monitoring drug dissolution. These results reflected the development of new crystalline species depending on the relative proportions of felodipine and xylitol with complete co-crystallization of felodipine being achieved in the presence of double its molar concentration of xylitol. This co-crystal formulation was compressed into sublingual tablet with ultrashort disintegration time with subsequent fast dissolution. Co-crystal formation was associated with enhanced dissolution with the optimum formulation producing the fastest dissolution rate. In conclusion, xylitol can be considered as a co-crystal co-former for enhanced dissolution rate of drugs.

Computational methods in drug discovery

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

Toxicity of daphnane-type diterpenoids from Genkwa Flos and their pharmacokinetic profile in rat

Daphnane-type diterpenoids (DDs) are the main types of plant diterpene orthoesters known and have remarkable biological activities. However, the in vivo toxicity and pharmacokinetic profile of DDs remains unkonwn. The aim of this study was to investigate the toxicity and pharmacokinetic profile of DDs from Genkwa Flos (Thymelaeaceae). The toxicity of diterpenoids was evaluated after oral administration of total diterpenoids extract from Genkwa Flos to rats, and the blood concentration of diterpenoids was analyzed by ultra performance liquid chromatography tandem triple-quadrupole mass spectrometry (UPLC-TQ-MS). The diterpenoids were confirmed to be the toxic components of Genkwa Flos. The pharmacokinetic profile of these diterpenoids was quite different due to their different structures. Although the contents of yuanhuafine and yuanhuapine were low in the extract, the blood concentrations were extremely high. In contrary, the contents of genkwanine F and Wikstroemia factor M1 in the extract were much higher, but they could not be detected in the blood. This result implied that yuanhuafine and yuanhuapine but not genkwanine F and Wikstroemia factor M1 were the potentail toxic components of Genkwa Flos in vivo. This paper shows for the first time the toxicity of diterpenoids from Genkwa Flos was correlated with their blood concentration and when DDs were used for medicinal purposes, their contents in herb as well as their blood concentrations should be considered.

Systems biological approach of molecular descriptors connectivity: optimal descriptors for oral bioavailability prediction

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.

Diverse approaches for the enhancement of oral drug bioavailability

In conscious and co-operating patients, oral drug delivery remains the preferable route of drug administration. However, not all drugs possess the desirable physicochemical and pharmacokinetic properties which favor oral administration mainly due to poor bioavailability. This has in some cases led to the choice of other routes of administration, which may compromise the convenience and increase the risk of non-compliance. Poor bioavailability has necessitated the administration of higher than normally required oral doses which often leads to economic wastages, risk of toxicity, erratic and unpredictable responses. The challenge over the years has been to design techniques that will allow oral administration of most drugs, irrespective of their properties, to achieve a therapeutic systemic availability. This will be a worthy achievement since over 90% of therapeutic compounds are known to possess oral bioavailability limitations. In this review, an attempt has been made to explore various approaches that have been used in recent years to improve oral drug bioavailability, including physical and chemical means. This review strives to provide a comprehensive overview of advances made over the past 10 years (2000-2010) in the improvement of the oral bioavailability of drugs. Briefly, the design of prodrugs to bypass metabolism or to enhance solubility as well as modification of formulation techniques such as the use of additives, permeation enhancers, solubilizers, emulsifiers and non-aqueous vehicles have been discussed. Arising approaches, such as formulation modification techniques; novel drug delivery systems, which exploit the gastrointestinal regionality of drugs, and include the pharmaceutical application of nanotechnology as an emerging area in drug delivery; inhibition of efflux pumps; and inhibition of presystemic metabolism have been more extensively addressed. This critical review sought to assess each method aimed at enhancing the oral bioavailability of drugs in terms of the purpose, scientific basis, limitations, commercial application, as well as the areas in which current research efforts are being focused and should be focused in the future.

A comparative study concerning chromatographic retention and computed partition coefficients of some precursors of peraza crown ethers

Retention indices for some precursors of peraza crown ethers were determined by reversed phase high-performance thin layer chromatography on RP-18 plates with methanol-water in different volume proportions as mobile phase. The Log P values for the same compounds were calculated using different computer programs: SciQSAR, SciLogP, Chem3D Ultra 8.0, XLOGP (based on atom contributions), Chemaxon and KOWWIN (based on atom/fragment contributions), cLogP (based on fragmental contributions), ALOGPS and IAlogP (based on atom-type electrotopological-state indices and neural network modeling). A comparative study concerning lipophilic parameters (RM0, b and ϕ0) and computed partition coefficients has been developed. Taking into account the correlation coefficients between determined and calculated Log P values, it seems that RM0 and b are less suitable than ϕ0 for estimating lipophilicity of the compounds investigated, and cLogP and ALOGPS provide the best correlations with experimental values.

Influence of molecular properties on oral bioavailability of lipophilic drugs - mapping of bulkiness and different measures of polarity

The biopharmaceutical assessment of new drug candidates based on their chemical structure is important in drug discovery and development. The scope of this study is to focus on lipophilic drugs and to clarify the role of their chemical predictors on oral bioavailability in humans. First their chemical properties were calculated from molecular modeling and the bioavailability data was obtained from the literature. The data was then analyzed by a partial least square method including non-linear terms. Significant coefficients were identified from a group of polarity- and solubility-related properties. Contour plots were constructed mapping molecular weight together with different polarity factors. Depending on the molecular weight a maximal bioavailability was found at solubility parameters of about 31-35 (J/cm(3))(1/2) and HLB values of roughly 4-12. The mapping of lipophilic drugs also revealed that a solubility parameter of less than 20 (J/cm(3))(1/2) or a HLB of smaller than unity is critical for the drug-likeness of new compounds.

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.

Structure-ADME relationship: still a long way to go?

BACKGROUND

Theoretical models for predicting absorption, distribution, metabolism and excretion (ADME) properties play increasingly important roles in support of the drug development process.

OBJECTIVE

We briefly review the in silico prediction models for three important ADME properties, namely, aqueous solubility, human intestinal absorption, and oral bioavailability.

METHODS

Rather than giving detailed descriptions of the ADME prediction models, we focus on the discussions of the prediction accuracies of the in silico models.

RESULTS/CONCLUSION

We find that the robustness and predictive capability of the ADME models are directly associated with the complexity of the ADME property. For the ADME properties involving complex phenomena, such as bioavailability, the in silico models usually cannot give satisfactory predictions. Moreover, the lack of large and high-quality data sets also greatly hinder the reliability of ADME predictions. While considerable progress has been achieved in ADME predictions, many challenges remain to be overcome.

Comparative evaluation of oral systemic exposure of 56 xenobiotics in rat, dog, monkey and human

The prediction of human pharmacokinetics is often based on in vivo preclinical pharmacokinetic data. However, to date, no clear guidance has been available about the relative ability of the major preclinical species to estimate human oral exposure. The study was conducted to survey the literature on oral pharmacokinetic parameters in rat, dog, monkey and human, and to compare various methods for prediction of oral exposure in humans. Fifty-six non-peptide xenobiotics were identified with oral pharmacokinetic data in rat, dog, monkey and human, and comparison of the data from each species to humans was conducted along with an evaluation of the molecular features of these compounds. Monkey liver blood flow-based oral exposure was qualitatively and quantitatively more predictive of human oral exposure than rat or dog. Furthermore, generation of data in three versus two preclinical species did not always improve human predictivity. The use of molecular properties did not substantially improve the prediction of human oral exposure compared with the prediction from monkey alone. These observations confirm the continued importance of non-human primates in preclinical pharmacokinetics, and also have implications for pharmacokinetic lead optimization and for prediction of human pharmacokinetic parameters from in vivo preclinical data.

Generation of a set of simple, interpretable ADMET rules of thumb

A set of simple, consistent structure-property guides have been determined from an analysis of a number of key ADMET assays run within GSK: solubility, permeability, bioavailability, volume of distribution, plasma protein binding, CNS penetration, brain tissue binding, P-gp efflux, hERG inhibition, and cytochrome P450 1A2/2C9/2C19/2D6/3A4 inhibition. The rules have been formulated using molecular properties that chemists intuitively know how to alter in a molecule, namely, molecular weight, logP, and ionization state. The rules supplement the more predictive black-box models available to us by clearly illustrating the key underlying trends, which are in line with reports in the literature. It is clear from the analyses reported herein that almost all ADMET parameters deteriorate with either increasing molecular weight, logP, or both, with ionization state playing either a beneficial or detrimental affect depending on the parameter in question. This study re-emphasizes the need to focus on a lower molecular weight and logP area of physicochemical property space to obtain improved ADMET parameters.

Application of linear discriminant analysis in the virtual screening of antichagasic drugs through trypanothione reductase inhibition

We have performed virtual screening to identify new lead trypanothione reductase inhibitor (TRI) compounds, enzyme present in Tripanozoma cruzi, the agent responsible of Chagas disease. From a training set of 58 compounds, linear discriminant analysis (LDA) was performed using 2D and 3D descriptors as discriminating variables in order to find out which function of descriptors characterizes the active TRI. The values of the statistical parameters F--Snedecor and Wilk's lambda for the discriminant function (DF) showed good statistical significance, as long as the rate of success in the prediction for both the training and the test set: 91.38% and 88.63%, in that order. Internal validation through the Leave--Group--Out methodology was performed with good results, assuring the stability of the DF. Afterwards, the DF was applied in virtual screening of 422,367 compounds. The optimum range of values of octanol--water partition coefficient for a compound to develop trypanothione reductase inhibition was applied as a second filtering criteria. 739 structurally heterogeneous drugs of the virtual library were selected as promissory TRI.

Predictive ADMET studies, the challenges and the opportunities

Predictive ADMET is the new 'hip' area in drug discovery. The aim is to use large databases of ADMET data associated with structures to build computational models that link structural changes with changes in response, from which compounds with improved properties can be designed and predicted. These databases also provide the means to enable predictions of human ADMET properties to be made from human in vitro and animal in vivo ADMET measurements. Both methods are limited by the amount of data available to build such predictive models, the limitations of modelling methods and our understanding of the systems we wish to model. The current failures, successes and opportunities are reviewed.