In vivo-in vitro-in silico pharmacokinetic modelling in drug development: current status and future directions

Diabetic wound healing of aloe vera major phytoconstituents through TGF-β1 suppression via in-silico docking, molecular dynamic simulation and pharmacokinetic studies

To restore the integrity of the skin and subcutaneous tissue, the wound healing process involves a complex series of well-orchestrated biochemical and cellular events. Due to the existence of various active components, accessibility and few side effects, some plant extracts and their phytoconstituents are recognised as viable options for wound healing agents. To find possible inhibitors of diabetic wound healing, four main constituents of aloe vera were identified from the literature. TGF-β1 and the compounds were studied using molecular docking to see how they interacted with the active site of target protein (PDB ID: 6B8Y). The pharmacokinetics investigation of the aloe emodin with the highest dock score complied with all the Lipinski's rule of five and pharmacokinetics criteria. Conformational change in the docked complex of Aloe emodin was investigated with the Amber simulation software, via a molecular dynamic (MD) simulation. The MD simulations of aloe emodin bound to TGF-β1 showed the significant structural rotations and twists occurring from 0 to 200 ns. The estimate of the aloe emodin-TGF-β1 complex's binding free energy has also been done using MM-PBSA/GBSA techniques. Additionally, aloe emodin has a wide range of enzymatic activities since their probability active (Pa) values is >0.700. 'Aloe emodin', an active extract of aloe vera, has been identified as the key chemical in the current investigation that can inhibit diabetic wound healing. Both in-vitro and in-vivo experiments will be used in a wet lab to confirm the current computational findings.Communicated by Ramaswamy H. Sarma.

From PROTAC to inhibitor: Structure-guided discovery of potent and orally bioavailable BET inhibitors

An X-ray structure of a CLICK chemistry-based BET PROTAC bound to BRD2(BD2) inspired synthesis of JQ1 derived heterocyclic amides. This effort led to the discovery of potent BET inhibitors displaying overall improved profiles when compared to JQ1 and birabresib. A thiadiazole derived 1q (SJ1461) displayed excellent BRD4 and BRD2 affinity and high potency in the panel of acute leukaemia and medulloblastoma cell lines. A structure of 1q co-crystalised with BRD4-BD1 revealed polar interactions with the AZ/BC loops, in particular with Asn140 and Tyr139, rationalising the observed affinity improvements. In addition, exploration of pharmacokinetic properties of this class of compounds suggest that the heterocyclic amide moiety improves drug-like features. Our study led to the discovery of potent and orally bioavailable BET inhibitor 1q (SJ1461) as a promising candidate for further development.

Naphthoquinones biflorin and bis-biflorin (Capraria biflora) as possible inhibitors of the fungus Candida auris polymerase: molecular docking, molecular dynamics, MM/GBSA calculations and in silico drug-likeness study

A new worldwide concern has emerged with the recent emergence of infections caused by Candida auris. This reflects its comparative ease of transmission, substantial mortality, and the increasing level of resistance seen in the three major classes of antifungal drugs. Efforts to create a better design for structure-based drugs that described numerous modifications and the search for secondary metabolic structures derived from plant species are likely to reduce the virulence of several fungal pathogens. In this context, the present work aimed to evaluate in silico two naphthoquinones isolated from the roots of Capraria biflora, biflorin, and its dimmer, bis-biflorin, as potential inhibitors of Candida auris polymerase. Based on the simulation performed with the two naphthoquinones, biflorin and bis-biflorin, it can be stated that bis-biflorin showed the best interactions with Candida auris polymerase. Still, biflorin also demonstrated favorable coupling energy. Predictive pharmacokinetic assays suggest that biflorin has high oral bioavailability and more excellent metabolic stability compared to the bis-biflorin analogue. constituting a promising pharmacological tool.Communicated by Ramaswamy H. Sarma.

SeDeM expert system with I-optimal mixture design for oral multiparticulate drug delivery: An encapsulated floating minitablets of loxoprofen Na and its in silico physiologically based pharmacokinetic modeling

Introduction: A SeDeM expert tool-driven I-optimal mixture design has been used to develop a directly compressible multiparticulate based extended release minitablets for gastro-retentive drug delivery systems using loxoprofen sodium as a model drug. Methods: Powder blends were subjected to stress drug-excipient compatibility studies using FTIR, thermogravimetric analysis, and DSC. SeDeM diagram expert tool was utilized to assess the suitability of the drug and excipients for direct compression. The formulations were designed using an I-optimal mixture design with proportions of methocel K100M, ethocel 10P and NaHCO₃ as variables. Powder was compressed into minitablets and encapsulated. After physicochemical evaluation lag-time, floating time, and drug release were studied. Heckel analysis for yield pressure and accelerated stability studies were performed as per ICH guidelines. The in silico PBPK Advanced Compartmental and Transit model of GastroPlus™ was used for predicting in vivo pharmacokinetic parameters. Results: Drug release follows first-order kinetics with fickian diffusion as the main mechanism for most of the formulations; however, a few formulations followed anomalous transport as the mechanism of drug release. The in-silico-based pharmacokinetic revealed relative bioavailability of 97.0%. Discussion: SeDeM expert system effectively used in QbD based development of encapsulated multiparticulates for once daily administration of loxoprofen sodium having predictable in-vivo bioavailability.

Predicting skin permeability using HuskinDB

A freely accessible database has recently been released that provides measurements available in the literature on human skin permeation data, known as the ‘Human Skin Database – HuskinDB’. Although this database is extremely useful for sourcing permeation data to help with toxicity and efficacy determination, it cannot be beneficial when wishing to consider unlisted, or novel compounds. This study undertakes analysis of the data from within HuskinDB to create a model that predicts permeation for any compound (within the range of properties used to create the model). Using permeability coefficient (K_p) data from within this resource, several models were established for K_p values for compounds of interest by varying the experimental parameters chosen and using standard physicochemical data. Multiple regression analysis facilitated creation of one particularly successful model to predict K_p through human skin based only on three chemical properties. The model transforms the dataset from simply a resource of information to a more beneficial model that can be used to replace permeation testing for a wide range of compounds.

Optimizing peptide inhibitors of SARS-Cov-2 nsp10/nsp16 methyltransferase predicted through molecular simulation and machine learning

Coronaviruses, including the recent pandemic strain SARS-Cov-2, use a multifunctional 2'-O-methyltransferase (2'-O-MTase) to restrict the host defense mechanism and to methylate RNA. The nonstructural protein 16 2'-O-MTase (nsp16) becomes active when nonstructural protein 10 (nsp10) and nsp16 interact. Novel peptide drugs have shown promise in the treatment of numerous diseases and new research has established that nsp10 derived peptides can disrupt viral methyltransferase activity via interaction of nsp16. This study had the goal of optimizing new analogous nsp10 peptides that have the ability to bind nsp16 with equal to or higher affinity than those naturally occurring. The following research demonstrates that in silico molecular simulations can shed light on peptide structures and predict the potential of new peptides to interrupt methyltransferase activity via the nsp10/nsp16 interface. The simulations suggest that misalignments at residues F68, H80, I81, D94, and Y96 or rotation at H80 abrogate MTase function. We develop a new set of peptides based on conserved regions of the nsp10 protein in the Coronaviridae species and test these to known MTase variant values. This results in the prediction that the H80R variant is a solid new candidate for potential new testing. We envision that this new lead is the beginning of a reputable foundation of a new computational method that combats coronaviruses and that is beneficial for new peptide drug development.

Pharmacokinetics-On-a-Chip: In Vitro Microphysiological Models for Emulating of Drugs ADME

Despite many ongoing efforts across the full spectrum of pharmaceutical and biotech industries, drug development is still a costly undertaking that involves a high risk of failure during clinical trials. Animal models played vital roles in understanding the mechanism of human diseases. However, the use of these models has been a subject of heated debate, particularly due to ethical matters and the inevitable pathophysiological differences between animals and humans. Current in vitro models lack the sufficient functionality and predictivity of human pharmacokinetics and toxicity, therefore, are not capable to fully replace animal models. The recent development of micro-physiological systems has shown great potential as indispensable tools for recapitulating key physiological parameters of humans and providing in vitro methods for predicting the pharmacokinetics and pharmacodynamics in humans. Integration of Absorption, Distribution, Metabolism, and Excretion (ADME) processes within one close in vitro system is a paramount development that would meet important unmet pharmaceutical industry needs. In this review paper, synthesis of the ADME-centered organ-on-a-chip technology is systemically presented from what is achieved to what needs to be done, emphasizing the requirements of in vitro models that meet industrial needs in terms of the structure and functions.

Clinical pharmacokinetic study of latrepirdine via in silico sublingual administration

In recent decades, numerous in silico methodologies have been developed focused on the study of pharmacodynamic, pharmacokinetics and toxicological properties of drugs. The study of the pharmacokinetic behavior of new chemical entities is an essential part of the successful development of a new drug and Gastroplus™ is a simulation software used to predict the pharmacokinetic behavior of chemical entities. Latrepirdine is a drug that has been studied for Alzheimer's disease and Huntington's disease and later abandoned by the pharmaceutical industry already in the clinical trials because it has not demonstrated therapeutic efficacy. During this project, through Gastroplus™ simulations, it was possible to achieve predicted values of C_max coincident with those found in clinical trials, showing its utility in the prediction of pharmacokinetic parameters. Besides, sublingual delivery has the potential to offer improved bioavailability by circumventing first-pass metabolism. This study used GastroPlus™ to simulate sublingual administration of latrepirdine and the results showed improvements in bioavailability and plasma concentrations achieved though this route of administration.

Requirements for Animal Experiments: Problems and Challenges

In vivo models remain a principle screening tool in the drug discovery pipeline. Here, the challenges associated with the need for animal experiments, as well as their impact on research, individual/societal, and economic contexts are discussed. A number of alternatives that, with further development, optimization, and investment, may replace animal experiments are also revised.

Development of a Hierarchical Support Vector Regression-Based In Silico Model for Caco-2 Permeability

Drug absorption is one of the critical factors that should be taken into account in the process of drug discovery and development. The human colon carcinoma cell layer (Caco-2) model has been frequently used as a surrogate to preliminarily investigate the intestinal absorption. In this study, a quantitative structure–activity relationship (QSAR) model was generated using the innovative machine learning-based hierarchical support vector regression (HSVR) scheme to depict the exceedingly confounding passive diffusion and transporter-mediated active transport. The HSVR model displayed good agreement with the experimental values of the training samples, test samples, and outlier samples. The predictivity of HSVR was further validated by a mock test and verified by various stringent statistical criteria. Consequently, this HSVR model can be employed to forecast the Caco-2 permeability to assist drug discovery and development.

Pharmacological insights and prediction of lead bioactive isolates of Dita bark through experimental and computer-aided mechanism

Dita bark (Alstonia scholaris (L.) R. Br.) is an ethnomedicine used for the management of various ailments. This study aimed to investigate the biological properties of methanol extract of A. scholaris bark (MEAS), through in vivo, in vitro and in silico approaches alongside its phytochemical profiling. Identification and nature of the bioactive secondary metabolites were studied by the established qualitative tests and GC-MS analysis. The antidepressant activity was determined by forced swimming test (FST) and tail suspension test (TST) in mice. The anti-inflammatory and thrombolytic effect was evaluated using inhibition of protein denaturation technique and clot lysis technique, respectively. Besides, computational studies of the isolated compounds and ADME/T analysis were performed by Schrödinger-Maestro (v11.1) software, and PASS prediction was conducted through PASS online tools. The GC-MS analysis revealed the presence of several secondary metabolites in MEAS. Treatment with MEAS revealed a significant reduction of immobility time in a dose-dependent manner in FST and TST. Besides, MEAS showed substantial anti-inflammatory effects at the higher dose (400 μg/mL) as well as revealed notable clot lysis effect as compared to control. In the case of computer-aided investigation, all compounds meet the condition of Lipinski's rule of five. PASS study also predicted for all compounds, and among these safe compound furazan-3-amine showed the most spontaneous binding energy for both antidepressant and thrombolytic activities, as well as 5-dimethylamino-6 azauracil, found promising for anti-inflammatory activity. Taken together, the investigation concludes that MEAS can be a potent source of antidepressant, anti-inflammatory, and thrombolytic agents.

In silico approach to study the metabolism and biological activities of oligomeric proanthocyanidin complexes

OBJECTIVES:

Over the past three decades, numerous studies have focused on the biological activities of oligomeric proanthocyanidins (OPCs) in the prevention of many diseases such as neurodegeneration, atherosclerosis, tumorigenesis, and microbial infections. OPC has redox-active metabolites which could modulate the intracellular redox equilibrium to maintain the antioxidant homeostasis. This redox-modulating efficiency of OPC could provide new insights into therapeutic approaches that could reduce the burden of cardiovascular diseases. The main objective of this study was to explore the biological and metabolic activities of OPC using in silico approaches.

METHODS:

To validate the above objective, chemoinformatic tools were used to predict the metabolism of OPC after ingestion, based on both the ligand and structure of the constituent compounds.

RESULTS:

OPC showed possible sites for Phase I metabolism by cytochrome P450, and the metabolites obtained thereafter may be responsible for its biological activities. Absorption, distribution, metabolism, elimination, and toxicity properties showed efficient absorption, distribution, and metabolism of OPC, without toxicity.

CONCLUSION:

Thus, from the results obtained, OPC could be strongly recommended as a cardioprotective drug.

In silico repositioning of approved drugs against Schistosoma mansoni energy metabolism targets

Schistosomiasis is a neglected parasitosis caused by Schistosoma spp. Praziquantel is used for the chemoprophylaxis and treatment of this disease. Although this monotherapy is effective, the risk of resistance and its low efficiency against immature worms compromises its effectiveness. Therefore, it is necessary to develop new schistosomicide drugs. However, the development of new drugs is a long and expensive process. The repositioning of approved drugs has been proposed as a quick, cheap, and effective alternative to solve this problem. This study employs chemogenomic analysis with use of bioinformatics tools to search, identify, and analyze data on approved drugs with the potential to inhibit Schistosoma mansoni energy metabolism enzymes. The TDR Targets Database, Gene DB, Protein, DrugBank, Therapeutic Targets Database (TTD), Promiscuous, and PubMed databases were used. Fifty-nine target proteins were identified, of which 18 had one or more approved drugs. The results identified 20 potential drugs for schistosomiasis treatment; all approved for use in humans.

Formulation and In Vitro, In Vivo Correlation Between Two Candesartan Cilexetil Tablets

In this study, the in vitro and in vivo interchangeability between generic candesartan 16 mg and the branded formulation was assessed. The in vitro release of these products was conducted in 3 pH media (1.2, 5.0, and 6.8), and similarity factors (f₂ ) were calculated. This bioequivalence study was a randomized, 2-period crossover study that included 42 healthy adult male subjects under fasting conditions with a 9-day washout. The pharmacokinetic (PK) parameters AUC_0-last , AUC_0-∞ , and C_max , t_max , and the elimination half-life time were assessed based on the plasma concentrations of candesartan, using a newly developed and validated liquid chromatography-tandem mass spectrometry bioanalytical method with acceptable degrees of linearity, sensitivity, precision, and accuracy. The geometric mean (ng·h/mL) of the AUC_0-∞ for the test and brand was 1595.49 and 1620.54, respectively, and the C_max (ng/mL) was 160.91 and 160.88, respectively. The 90%CIs of geometric mean ratios (test-to-reference ratios) were 98.26%, 98.45%, and 99.86% for AUC_0-last , AUC_0-∞ , and C_max respectively. These PK parameters lie within the US Food and Drug Administration- and European Medicines Agency-specified bioequivalence limit (80%-125%). Both products were well tolerated by all the subjects. The tested drug product was bioequivalent to the reference drug and had the same safety profile.

Challenges and opportunities for the future of monoclonal antibody development: Improving safety assessment and reducing animal use

The market for biotherapeutic monoclonal antibodies (mAbs) is large and is growing rapidly. However, attrition poses a significant challenge for the development of mAbs, and for biopharmaceuticals in general, with large associated costs in resource and animal use. Termination of candidate mAbs may occur due to poor translation from preclinical models to human safety. It is critical that the industry addresses this problem to maintain productivity. Though attrition poses a significant challenge for pharmaceuticals in general, there are specific challenges related to the development of antibody-based products. Due to species specificity, non-human primates (NHP) are frequently the only pharmacologically relevant species for nonclinical safety and toxicology testing for the majority of antibody-based products, and therefore, as more mAbs are developed, increased NHP use is anticipated. The integration of new and emerging in vitro and in silico technologies, e.g., cell- and tissue-based approaches, systems pharmacology and modeling, have the potential to improve the human safety prediction and the therapeutic mAb development process, while reducing and refining animal use simultaneously. In 2014, to engage in open discussion about the challenges and opportunities for the future of mAb development, a workshop was held with over 60 regulators and experts in drug development, mechanistic toxicology and emerging technologies to discuss this issue. The workshop used industry case-studies to discuss the value of the in vivo studies and identify opportunities for in vitro technologies in human safety assessment. From these and continuing discussions it is clear that there are opportunities to improve safety assessment in mAb development using non-animal technologies, potentially reducing future attrition, and there is a shared desire to reduce animal use through minimised study design and reduced numbers of studies.

Opportunities to Apply the 3Rs in Safety Assessment Programs

Before a potential new medicine can be administered to humans it is essential that its safety is adequately assessed. Safety assessment in animals forms an integral part of this process, from early drug discovery and initial candidate selection to the program of recommended regulatory tests in animals. The 3Rs (replacement, reduction, and refinement of animals in research) are integrated in the current regulatory requirements and expectations and, in the EU, provide a legal and ethical framework for in vivo research to ensure the scientific objectives are met whilst minimizing animal use and maintaining high animal welfare standards. Though the regulations are designed to uncover potential risks, they are intended to be flexible, so that the most appropriate approach can be taken for an individual product. This article outlines current and future opportunities to apply the 3Rs in safety assessment programs for pharmaceuticals, and the potential (scientific, financial, and ethical) benefits to the industry, across the drug discovery and development process. For example, improvements to, or the development of, novel, early screens (e.g., in vitro, in silico, or nonmammalian screens) designed to identify compounds with undesirable characteristics earlier in development have the potential to reduce late-stage attrition by improving the selection of compounds that require regulatory testing in animals. Opportunities also exist within the current regulatory framework to simultaneously reduce and/or refine animal use and improve scientific outcomes through improvements to technical procedures and/or adjustments to study designs. It is important that approaches to safety assessment are continuously reviewed and challenged to ensure they are science-driven and predictive of relevant effects in humans.

Treating disorders of the neonatal central nervous system: pharmacokinetic and pharmacodynamic considerations with a focus on antiepileptics

A major consideration in the treatment of neonatal disorders is that the selected drug, dose and dosage frequency is safe, effective and appropriate for the intended patient population. Thus, a thorough knowledge of the pharmacokinetics and pharmacodynamics of the chosen drug within the patient population is essential. In paediatric and neonatal populations two additional challenges can often complicate drug treatment - the inherently greater physiological variability, and a lack of robust clinical evidence of therapeutic range. There has traditionally been an overreliance in paediatric medicine on extrapolating doses from adult values by adjusting for bodyweight or body surface area, but many other sources of variability exist which complicate the choice of dose in neonates. The lack of reliable drug dosage data in neonates has been highlighted by regulatory authorities, as only ~50% of the most commonly used paediatric medicines have been examined in a paediatric population. Moreover, there is a paucity of information on the pharmacokinetic parameters which affect drug concentrations in different body tissues, and pharmacodynamic responses to drugs in the neonate. Thus, in the present review, we draw attention to the main pharmacokinetic factors that influence the unbound brain concentration of neuroactive drugs. Moreover, the pharmacodynamic differences between neonates and adults that affect the activity of centrally-acting therapeutic agents are briefly examined, with a particular emphasis on antiepileptic drugs.

Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes

The adverse effects to humans and environment of only few chemicals are well known. Absorption, distribution, metabolism, and excretion (ADME) are the steps of pharmaco/toxicokinetics that determine the internal dose of chemicals to which the organism is exposed. Of all the xenobiotic-metabolizing enzymes, the cytochrome P450 (CYP) enzymes are the most important due to their abundance and versatility. Reactions catalyzed by CYPs usually turn xenobiotics to harmless and excretable metabolites, but sometimes an innocuous xenobiotic is transformed into a toxic metabolite. Data on ADME and toxicity properties of compounds are increasingly generated using in vitro and modeling (in silico) tools. Both physics-based and empirical modeling approaches are used. Numerous ligand-based and target-based as well as combined modeling methods have been employed to evaluate determinants of CYP ligand binding as well as predicting sites of metabolism and inhibition characteristics of test molecules. In silico prediction of CYP–ligand interactions have made crucial contributions in understanding (1) determinants of CYP ligand binding recognition and affinity; (2) prediction of likely metabolites from substrates; (3) prediction of inhibitors and their inhibition potency. Truly predictive models of toxic outcomes cannot be created without incorporating metabolic characteristics; in silico methods help producing such information and filling gaps in experimentally derived data. Currently modeling methods are not mature enough to replace standard in vitro and in vivo approaches, but they are already used as an important component in risk assessment of drugs and other chemicals.

In vitro-in vivo correlation of efavirenz tablets using GastroPlus®

The aim of the present work was to use GastroPlus™ software for the prediction of pharmacokinetic profiles and in vitro-in vivo correlation (IVIVC) as tools to optimize the development of new generic medications. GastroPlus™ was used to simulate the gastrointestinal compartment and was based on the advanced compartmental absorption and transit model. Powder dissolution and efavirenz tablet dissolution studies were carried out to generate data from which correlation was established. The simulated plasma profile, based on the physicochemical properties of efavirenz, was almost identical to that observed in vivo for biobatches A and B. A level A IVIVC was established for the dissolution method obtained for the generic candidate using the Wagner-Nelson (r (2) = 0.85) and for Loo-Riegelman models (r(2) = 0.92). The percentage of fraction absorbed indicated that 0.5% sodium lauryl sulfate may be considered a biorelevant dissolution medium for efavirenz tablets. The simulation of gastrointestinal bioavailability and IVIVC obtained from immediate-release tablet formulations suggests that GastroPlus™ is a valuable in silico method for IVIVC and for studies directed at developing formulations of class II drugs.

Development of a computational tool to rival experts in the prediction of sites of metabolism of xenobiotics by p450s

The metabolism of xenobiotics--and more specifically drugs--in the liver is a critical process controlling their half-life. Although there exist experimental methods, which measure the metabolic stability of xenobiotics and identify their metabolites, developing higher throughput predictive methods is an avenue of research. It is expected that predicting the chemical nature of the metabolites would be an asset for designing safer drugs and/or drugs with modulated half-lives. We have developed IMPACTS (In-silico Metabolism Prediction by Activated Cytochromes and Transition States), a computational tool combining docking to metabolic enzymes, transition state modeling, and rule-based substrate reactivity prediction to predict the site of metabolism (SoM) of xenobiotics. Its application to sets of CYP1A2, CYP2C9, CYP2D6, and CYP3A4 substrates and comparison to experts' predictions demonstrates its accuracy and significance. IMPACTS identified an experimentally observed SoM in the top 2 predicted sites for 77% of the substrates, while the accuracy of biotransformation experts' prediction was 65%. Application of IMPACTS to external sets and comparison of its accuracy to those of eleven other methods further validated the method implemented in IMPACTS.