Prediction Of Pharmacokinetic Parameters Research Articles

Synthesis of Functionalized Isoxazolines as New Acetylcholinesterase and Tyrosinase Inhibitors and Antioxidant Agents

In the search for new leads capable of interacting with multiple targets involved in NDD pathogenesis, a series of pyrazine-linked isoxazoline scaffolds were designed, synthesized and evaluated for their acetylcholinesterase and tyrosinase inhibitory potency and antioxidant activity. Isoxazolines 4a, 4d and 4h exhibited better molecular interaction with cholinesterases, tyrosinases and peroxiredoxin enzymes. Isoxazolines 4a, 4d and 4h interacted with acetylcholinesterase with the highest docking score of −9.083, −8.68 and −7.87 kcal/mol, respectively. Compound 4h ranked top when interacting with butyrylcholinesterase with a docking score of −7.926 kcal/mol, followed by 4a (−6.327 kcal/mol). 4a exhibited a robust interaction with 1HD2 with a docking score of −3.103 kcal/mol followed by 4d and 4h. 4a, 4d and 4h exhibited better docking scores of −5.47 kcal/mol, −4.63 kcal/mol and −5.157 kcal/mol with the enzyme tyrosinase. Based on the in-silico data, we have proceeded further to synthesis and in-vitro studies. Chalcones were synthesized by the Claisen-Schmidt reaction, which was cyclised to isoxazolines by the cycloaddition of hydroxylamine HCl. FTIR, 1HNMR, 13CNMR, and mass spectral studies further characterized the compounds. The prediction of pharmacokinetic parameters also supports the study, and all the compounds passed the screening. In-vitro studies were performed to evaluate acetylcholinesterase and tyrosinase inhibition. Compound 4h displayed excellent action against acetylcholinesterases and tyrosinase enzymes. Hydrogen peroxide assay determined the antioxidant effect, which found that 4h and 4d compounds exhibited higher strength as peroxide scavengers. Thus, the study shows that pyrazine-based isoxazolines with electron-withdrawing groups can be used as leads to develop a drug of choice for NDD, as it has excellent acetylcholinesterase and tyrosinase inhibitory action and tremendous peroxide scavenging effect.

Application of Machine Learning Technology in the Prediction of ADME- Related Pharmacokinetic Parameters.

As an important determinant in drug discovery, the accurate analysis and acquisition of pharmacokinetic parameters are very important for the clinical application of drugs. At present, the research and development of new drugs mainly obtain their pharmacokinetic parameters through data analysis, physiological model construction and other methods, but the results are often quite different from the actual situation, needing more manpower and material resources. We mainly discuss the application of machine learning technology in the prediction of pharmacokinetic parameters, which are mainly related to the quantitative study of drug absorption, distribution, metabolism and excretion in the human body, such as bioavailability, clearance, apparent volume of distribution and so on. This paper first introduces the pharmacokinetic parameters, the relationship between the quantitative structure-activity relationship model and machine learning, then discusses the application of machine learning technology in different prediction models, and finally discusses the limitations, prospects and future development of the machine learning model in predicting pharmacokinetic parameters. Unlike traditional pharmacokinetic analysis, machine learning technology can use computers and algorithms to speed up the acquisition of pharmacokinetic parameters to varying degrees. It provides a new idea to speed up and shorten the cycle of drug development, and has been successfully applied in drug design and development. The use of machine learning technology has great potential in predicting pharmacokinetic parameters. It also provides more choices and opportunities for the design and development of clinical drugs in the future.

Evaluation of Free Online ADMET Tools for Academic or Small Biotech Environments.

For a new molecular entity (NME) to become a drug, it is not only essential to have the right biological activity also be safe and efficient, but it is also required to have a favorable pharmacokinetic profile including toxicity (ADMET). Consequently, there is a need to predict, during the early stages of development, the ADMET properties to increase the success rate of compounds reaching the lead optimization process. Since Lipinski's rule of five, the prediction of pharmacokinetic parameters has evolved towards the current in silico tools based on empirical approaches or molecular modeling. The commercial specialized software for performing such predictions, which is usually costly, is, in many cases, not among the possibilities for research laboratories in academia or at small biotech companies. Nevertheless, in recent years, many free online tools have become available, allowing, more or less accurately, for the prediction of the most relevant pharmacokinetic parameters. This paper studies 18 free web servers capable of predicting ADMET properties and analyzed their advantages and disadvantages, their model-based calculations, and their degree of accuracy by considering the experimental data reported for a set of 24 FDA-approved tyrosine kinase inhibitors (TKIs) as a model of a research project.

MACHINE LEARNING-BASED VIRTUAL SCREENING, MOLECULAR DOCKING, DRUG-LIKENESS, PHARMACOKINETICS AND TOXICITY ANALYSES TO IDENTIFY NEW NATURAL INHIBITORS OF THE GLYCOPROTEIN SPIKE (S1) OF SARS-CoV-2

To identify natural bioactive compounds (NBCs) as potential inhibitors of spike (S1) by means of in silico assays. NBCs with previously proven biological in vitro activity were obtained from the ZINC database and analyzed through virtual screening and molecular docking to identify those with higher affinity to the spike protein. Eight machine learning models were used to validate the results: Principal Component Analysis (PCA), Artificial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Partial Least Squares-Discriminant Analysis (PLS-DA), Gradient Boosted Tree Discriminant Analysis (XGBoostDA), Soft Independent Modelling of Class Analogies (SIMCA) and Logistic Regression Discriminate Analysis (LREG). Selected NBCs were submitted to drug-likeness prediction using Lipinski’s and Veber’s rule of five. A prediction of pharmacokinetic parameters and toxicity was also performed (ADMET). Antivirals currently used for COVID-19 (remdesivir and molnupiravir) were used as a comparator. A total of 170,906 compounds were analyzed. Of these, 34 showed greater affinity with the S1 (affinity energy < -7 kcal mol-1). Most of these compounds belonged to the class of coumarins (benzopyrones), presenting a benzene ring fused to a lactone (group of heterosides). The PLS-DA model was able to reproduce the results of the virtual screening and molecular docking (accuracy of 97.0%). Of the 34 compounds, only NBC5 (feselol), NBC14, NBC15 and NBC27 had better results in ADMET predictions. These had similar binding affinity to S1 when compared to remdesivir and molnupirvir. Feselol and three other NBCs were the most promising candidates for treating COVID-19. In vitro and in vivo studies are needed to confirm these findings.

Synthesis, in silico ADME, toxicity prediction and molecular docking studies of N-substituted [1,2,4]triazolo[4,3-a]pyrazine derivatives as potential anticonvulsant agents

Two new series of N-substituted [1,2,4]triazolo[4,3-a]pyrazine derivatives, 2a-e and 3a-e were synthesized by coupling substituted acid chlorides/benzyl chlorides with 3-(trifluoromethyl)-5,6,7,8-tetrahydro-[1,2,4]triazolo[4,3-a]pyrazine and investigated for antioxidant, anticonvulsant effect and neurotoxicity. The in vitro antioxidant activity was assessed using the DPPH and hydroxyl radical scavenging assays. The anticonvulsant potential of the synthesised molecules was assessed using MES and PTZ tests, and their neurotoxicity was evaluated using a rotorod test in mice. Compounds 2a, 2b, 2e, and 3d were identified to have a good anti-seizure action while causing minimal neurotoxicity. The most effective anticonvulsants were compounds 2a, 2b, and 2e, with median effective dosages of 0.38, 0.36, and 0.25 mg/kg, respectively. A computational analysis was also conducted, which included drug-likeness and docking studies, as well as prediction of pharmacokinetic parameters. Docking studies of potent compounds in the binding pocket of the 4-aminobutyrate aminotransferase protein (PDB ID: 1OHV) reveal strong binding interactions and high docking scores. From these findings, the potent compounds 2a, 2b, and 2e might be a promising lead in the quest for safer and more effective anticonvulsants with neuroprotective properties.

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 Cmax 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.

Synthesis and anticonvulsant evaluation of indoline derivatives of functionalized aryloxadiazole amine and benzothiazole acetamide

A series of N-(substituted benzothiazole-2-yl)-2-(2,3-dioxoindolin-1-yl)acetamide (4a-i) and substituted-[3-((5-phenyl-1,3,4-oxadiazole-2-yl)imino)indolene-2-one] (5a-f) were designed, synthesized fulfilling the structural requirement of pharmacophore and evaluated for anticonvulsant activities using maximal electroshock test (MES), subcutaneous pentylenetetrazole (scPTZ) seizures and neurotoxicity by motor impairment model in mice. The most active compoundN-(5-chlorobenzo[d]thiazol-2-yl)-2-(2,3-dioxoindolin-1-yl)acetamide (4a) has shown significant anticonvulsant activity against both MES and scPTZ screens and emerged as most effective anticonvulsant compound with median dose of 35.7 mg/kg (MES ED50), 88.15 mg/kg (scPTZ ED50) and toxic dose (TD50) was found to be > 500mg/kg. In silico studies including molecular docking study was carried to establish the molecular interaction of potent compound (4a) in both Na+ channel and GABAA receptors. The prediction of pharmacokinetic parameters and distance mapping of compounds were also performed to establish the drug likeness property.

Structure based pharmacophore modelling approach for the design of azaindole derivatives as DprE1 inhibitors for tuberculosis

Cell wall of mycobacterium acts as a primary interface which helps in the regulation of important functions and also aids to pathogenicity and virulence of the organism, making it a crucial target for drug discovery. Decaprenylphosphoryl-d-ribose 2′-epimerase (DprE), is important for the growth and survival of Mycobacterium tuberculosis. DprE1 is a donor of arabinose sugars which helps in the formation of cell wall components-lipoarabinomannan and arabinogalactan through Decaprenyl-phosphoryl d-arabinose (DPA) pathway. In our study, we have chosen Azaindole derivatives as DprE1 inhibitors which possess non-covalent property. TBA7371 (azaindole derivative, non-covalent inhibitor) is currently in first phase of clinical trials as DprE1 inhibitor. Azaindoles have been found to be equally potent against drug-sensitive and isoniazid/rifampin-resistant strains. Hence, azaindoles are an attractive class for further optimization as potential DprE1 inhibitors for TB. Structure-based pharmacophore model was generated to investigate the compounds with similar molecular features. Compounds having a good fitness score and pharmacophoric features were compared with the molecules in clinical trial and were proceeded for molecular docking studies to identify the binding affinity of the compounds with target protein DprE1. Energy based calculations using Prime MM-GBSA of Schrodinger was further executed to examine free binding energy of the ligands. The prediction of pharmacokinetic parameters (ADME) plays an important role to identify safe and potent molecules which may further have potential to become drug candidates. Induced-fit docking approach and Molecular Dynamics integrated with Prime MM-GBSA calculations of both hit compounds has further confirmed the binding affinity and stability. All the results obtained from our study were interpreted and compared with DprE1 inhibitor in clinical trials. Study identified ZINC000170252277 as a potential hit compound for further biological evaluation as DprE1 inhibitor.

Pyridine and Benzoisothiazole Decorated Vanillin Chalcones: Synthesis, Antimicrobial, Antioxidant, Molecular Docking Study and ADMET Properties.

The search for new antimicrobial drugs is a never-ending task due to microbial resistance to the existing drugs. Antioxidants are essential to prevent free radical reactions which lead to chronic diseases to humankind. The present studies were aimed at synthesis, characterization, antimicrobial and antioxidant activities of pyridine and benzoisothiazole decorated chalcones. FTIR spectra were recorded using KBr pellets on Shimadzu FT-IR spectrophotometer. 1H and 13C NMR spectra were recorded on Bruker 400 MHz spectrometer. Antimicrobial activity of the synthesized chalcones was found to be good against different bacterial and fungal strains. Antioxidant activity was studied in terms of 2,2-diphenyl-1-picrylhydrazyl, hydroxyI and superoxide radical scavenging activities. Molecular docking was studied using Discovery Studio Visualizer Software, version 16 whereas Autodock Vina program was used to predict the toxicity profile of the compounds using FAFDrugs2 predictor. The compounds 5c, 5d & 6c showed good antioxidant activities. The insilico molecular docking study supports the experimental results and demonstrated that the chalcones 5d, 6a and 7a are the most active among the synthesized derivatives. Prediction of pharmacokinetic parameters and molecular docking studies suggest that the synthesized chalcones have good pharmacokinetic properties to act as lead molecules in the drug discovery process.

NATURAL COMPOUNDS FROM DJIBOUTIAN MEDICINAL PLANTS AS INHIBITORS OF COVID-19 BY IN SILICO INVESTIGATIONS

Objective: The new coronavirus type SARS-Cov 2 (severe acute respiratory syndrome), which appeared in autumn 2019 in China, became a global pandemic in a few months. In this work, we looked for the potential anti SARS-Cov 2 of the compounds isolated from three Djiboutian medicinal plants, namely Acacia seyal, Cymbopogon commutatus, and Indigofera caerulea.
 Methods: We carried out a molecular docking with nine biomolecules, β-Sitosterol, Quercetin, Catechin, Lupeol, Rutin, Kaempferol, Gallic acid, Piperitone and Limonene on three target sites which are SARS-CoV-2 main protease (Mp), SARS-CoV-2 receptor-binding domain (RBD) and human furin protease. These targets are chosen because of their role in the process of penetration of the virus into human cells and its multiplication. Moreover, the predictions of pharmacokinetic parameters as well as toxicological properties have been determined using an online bioinformatics tool named SwissADME and AdmetSAR respectively.
 Results: The phenolic compounds have a very good affinity on these three target sites with binding energies of up to-9.098 kcal/mol for rutin on SARS-CoV-2 Mp, much better than the two reference drugs hydroxychloroquine (-5.816 kcal/mol) and remdesivir (-7.194 kcal/mol). Except for β-Sitosterol, the tested biomolecules have weak toxicity.
 Conclusion: These natural compounds can be used against covid 19 pending In vitro and In vivo evaluations.

Hit identification and drug repositioning of potential non-nucleoside reverse transcriptase inhibitors by structure-based approach using computational tools (part II)

AIDS is a global infection involving several complications and its increasing prevalence every year has prioritized our study. Therapy associated with HIV has led to emergence of multidrug resistance and toxicity. Thus, the development of a potent, affordable and safe anti-HIV drug is a global concern. Among the different targets developed, inhibition of non-nucleoside reverse transcriptase (NNRT) is found to be effective and promising. Etravirine, efavirenz, nevirapine, rilpivirine and delavirdine are the marketed NNRTIs available. This study is focused on computational prediction of hit molecules as well as repurposing of various FDA-approved drugs as potential NNRTIs. A synthetic database from ZINCpharmer, publicly available natural databases of coumarins, chromones and chalcones, and two databases of FDA-approved drugs for repurposing were screened to check for the possibility of these compounds to possess anti-HIV activity. Study utilizes a structure-based approach with the generated pharmacophore of target protein (PDB ID: 3MEC), screening of selected datasets is carried out using the Phase tool of Schrodinger. The top filtered compounds with good fitness score were proceeded to molecular docking studies to study their binding affinity to the target. Energy-based calculations using Prime MM-GBSA of Schrodinger was performed to determine free binding energy of the complexes. Prediction of pharmacokinetic parameters of top compounds is further carried out and reported. All the results obtained from different databases are compiled, interpreted and five molecules were subjected to molecular dynamic studies to further confirm the prediction and identified hit molecules for in vitro screening as potential NNRTIs.Communicated by Ramaswamy H. Sarma

Interpretation of Non-Clinical Data for Prediction of Human Pharmacokinetic Parameters: In Vitro-In Vivo Extrapolation and Allometric Scaling.

Extrapolation of pharmacokinetic (PK) parameters from in vitro or in vivo animal to human is one of the main tasks in the drug development process. Translational approaches provide evidence for go or no-go decision-making during drug discovery and the development process, and the prediction of human PKs prior to the first-in-human clinical trials. In vitro-in vivo extrapolation and allometric scaling are the choice of method for projection to human situations. Although these methods are useful tools for the estimation of PK parameters, it is a challenge to apply these methods since underlying biochemical, mathematical, physiological, and background knowledge of PKs are required. In addition, it is difficult to select an appropriate methodology depending on the data available. Therefore, this review covers the principles of PK parameters pertaining to the clearance, volume of distribution, elimination half-life, absorption rate constant, and prediction method from the original idea to recently developed models in order to introduce optimal models for the prediction of PK parameters.

Prediction of Clearance and Dose of Midazolam in Preterm and Term Neonates: A Comparative Study Between Allometric Scaling and Physiologically Based Pharmacokinetic Modeling

Children are not small adults because besides size there are subtle physiological and biochemical differences between children and adults. Like adults, children also require medicine for the management or cure for the underlying diseases. To select a right dose in children, pharmacokinetic (PK) information is warranted. However, in many instances, a PK study in neonates and infants may not be possible. Therefore, various methods are used to predict PK parameters in this group of population, and these predicted parameters may help to calculate a safe dose for the very young children. Allometry is widely used for the prediction of PK parameters in children and subsequently one can predict dose from these predicted PK parameters. Physiologically based pharmacokinetic modeling (PBPK) has also become a useful tool to achieve these goals. Therefore, the objective of this study was to compare the predictive performance of allometry and PBPK for a test compound, midazolam in preterm, and term neonates. In this study, there were 5 preterm neonates (gestational age ranging from 34 to 37 weeks) and 5 term neonates (gestational age ranging from 38 to 41 weeks). PBPK modeling was performed using PK-Sim 6.0 and clearance, as well as midazolam dose in neonates was predicted. Clearance and midazolam dose in neonates was also predicted by allometric scaling. In this study, the allometric exponents for the prediction of midazolam clearance in preterm neonates and term neonates were 1.2 and 1.1, respectively. Similarly, for the prediction of midazolam neonatal dose, the exponent of allometry was either 0.9 or 1.0. The predicted midazolam clearance and dose by both methods were then compared with observed midazolam clearance and dose in neonates. The results of the study showed a slightly better prediction of midazolam clearance in neonates by PBPK than allometric scaling. However, the projected dose of midazolam in neonates was comparable between the 2 methods. Overall, it was noted that both PBPK and allometric model can be used to predict clearance and dose of midazolam in neonates.

Effect on Acetylcholinesterase and Anti-oxidant Activity of Synthetic Chalcones having a Good Predicted Pharmacokinetic Profile.

Acetylcholinesterase (AChE) is an important target in the development of drug to treat Alzheimer's disease (AD). In this work, we investigated the effect of twenty-two synthesized chalcones on AChE activity. This work is aimed to synthesize and evaluate the effect of chalcones on the AChE activity, as well as anti-oxidant activity and predict their pharmacokinetic profile. Chalcones were synthesized through a Claisen-Schmidt condensation and their inhibitory effect on the AChE was evaluated by the Elmann's colorimetric method. To determine the anti-oxidant activity the DPPH radical scavenging method was chosen. We found that all chalcones inhibit this activity, with IC50 values ranging from 0.008 to 4.8 µM. We selected the most active compound 19 with an IC50 value of 0.008 µM for a kinetic study demonstrating a competitive inhibition mode. Molecular docking simulations showed a good interaction between 19 and the active site of AChE. Considering the prediction of pharmacokinetic parameters being a useful tool for selecting potential drug candidates, our study results suggest that the majority of chalcones, including the most active one, have a promising pharmacokinetic profile and blood-brain barrier permeability. The involvement of reactive oxygen species (ROS) in AD-related events has encouraged us to evaluate these chalcones as radical scavengers. We have found that compound 19 is a potent AChE inhibitor, and based on kinetic studies, it acts as a competitive inhibitor.

Application of physiologically‐based pharmacokinetic modeling in hepatic impairment populations (1064.9)

Diagnosis of hepatic impairment (HI) is associated with a reduction in hepatocyte expression, a decrease in albumin and α‐1‐acid glycoprotein and a reduction in renal function, the combination of which can result in alteration of the pharmacokinetic (PK) properties of a drug. Therefore, regulatory agencies such as the U.S. Food and Drug Administration often require the assessment of PK parameters in HI populations. These studies can entail single or multiple‐dose approaches and must be of sufficient duration to capture the terminal half‐life of the drug, a parameter which may be extended in HI populations. The use of physiologically‐based pharmacokinetic (PBPK) modeling, a technique combining demographic and genetic factors, anatomical and physiological parameters and drug‐specific properties, can aid in the prediction of PK parameters and the design of clinical trials in HI populations. To that end, the utility of PBPK modeling in predicting unique clinical scenarios in HI populations was evaluated for a number of compounds including sensitive P450 substrates and drugs cleared by multiple P450s. Drug interactions, enantioselective PK changes and effects on hepatic versus intestinal CYP3A4 metabolism due to HI were predicted. Results suggest that PBPK modeling is a useful tool for predicting changes in PK parameters under multiple conditions and for contributing to the design of HI clinical trials.

Evaluation of a Generic Physiologically Based Pharmacokinetic Model for Lineshape Analysis

The mechanistic framework of physiologically based pharmacokinetic (PBPK) models makes them uniquely suited to hypothesis testing and lineshape analysis, which help provide valuable insights into mechanisms that contribute to the observed concentration-time profiles. The aim of this article is to evaluate the utility of PBPK models for simulating oral lineshapes by optimizing clearance and distribution parameters through fitting observed intravenous pharmacokinetic profiles. A generic PBPK model, built in-house using MATLAB software and incorporating absorption, metabolism, distribution, biliary and renal elimination models, was employed for simulation of the concentration-time profiles of nine marketed drugs with diverse physicochemical and pharmacokinetic profiles and absorption rates determined solely by transcellular or paracellular permeability and solubility. The model is based on easily available physicochemical properties of compounds such as the log P, acid dissociation constant and solubility, and in vitro pharmacokinetic data such as Caco-2 permeability, the fraction of the compound unbound in plasma, and microsomal or hepatocyte intrinsic clearance. Clearance and distribution parameters optimized through simulation of intravenous profiles were used to simulate their corresponding oral profiles, which are determined by a multitude of parameters, both compound-dependent and physiological. Comparison of the simulated and observed oral profiles was done using goodness-of-fit parameters such as the reduced chi(2) statistic. Fold errors were calculated for the area under the plasma concentration-time curve (AUC), maximum plasma concentration (C(max)) and time to reach the C(max) (t(max)), to assess the accuracy of predictions. The approach of predicting the oral profiles by optimizing the clearance and distribution parameters using the observed intravenous profile seemed to perform well for the nine compounds chosen for the study. The mean fold error for oral pharmacokinetic parameters, such as the C(max), t(max) and AUC, and for lineshape simulation was within 2-fold. The validation of the generic PBPK model built in-house demonstrated that as long as the absorption profile of a compound is determined solely by solubility and paracellular or transcellular permeability, the PBPK simulations of oral profiles using optimized parameters from intravenous simulations provide reasonably good agreement with the observed profile with respect to both the lineshape fit and prediction of pharmacokinetic parameters. Therefore, any lineshape mismatch between PBPK simulated and observed oral profiles can be interpreted suitably to gain mechanistic insights into the pharmacokinetic processes that have resulted in the observed lineshape. A strategy has been proposed to identify involvement of carrier-mediated transport; clearance saturation; enterohepatic recirculation of the parent compound; extra-hepatic, extra-gut elimination; higher in vivo solubility than predicted in vitro; drug-induced gastric emptying delays; gut loss and regional variation in gut absorption.

Metabolism Profiling, and Cytochrome P450 Inhibition & Induction in Drug Discovery

To reduce the high attrition rates of NCEs in preclinical and clinical development uncovering pharmacokinetics, toxicokinetics, drug metabolism, and drug-drug interactions early in drug discovery would be highly valuable. There have been many in vitro screens developed for these areas that have higher sample throughput, which is consistent with the iterative cycle of a typical drug discovery research project. We have presented the present status and given detailed descriptions of biotransformation, metabolic stability assays, identification of drug metabolizing P450 enzymes, prediction of pharmacokinetic parameters from in vitro metabolism data, structure elucidation of metabolites, CYP450 inhibition assays and CYP450 induction assays from a drug discovery perspective. Strategies for the proper sequencing of primary and secondary assays employedfor drug metabolism and CYP450 inhibition & induction is discussed.

Prediction of pharmacokinetic parameters and the assessment of their variability in bioequivalence studies by artificial neural networks.

The methodology of predicting the pharmacokinetic parameters (AUC, cmax, tmax) and the assessment of their variability in bioequivalence studies has been developed with the use of artificial neural networks. The data sets included results of 3 distinct bioequivalence studies of oral verapamil products, involving a total of 98 subjects and 312 drug applications. The modeling process involved building feedforward/backpropagation neural networks. Models for pharmacokinetic parameter prediction were also used for the assessment of their variability and for detecting the most influential variables for selected pharmacokinetic parameters. Variables of input neurons based on logistic parameters of the bioequivalence study, clinical-biochemical parameters, and the physical examination of individuals. The average absolute prediction errors of the neural networks for AUC, cmax, and tmax prediction were: 30.54%, 39.56% and 30.74%, respectively. A sensitivity analysis demonstrated that for verapamil the three most influential variables assigned to input neurons were: total protein concentration, aspartate aminotransferase (AST) levels, and heart-rate for AUC, AST levels, total proteins and alanine aminotransferase (ALT) levels, for cmax, and the presence of food, blood pressure, and body-frame for tmax. The developed methodology could supply inclusion or exclusion criteria for subjects to be included in bioequivalence studies.

迅速定量法（ＦＰＩＡ法）による急性ベンゾジアゼピン系薬物中毒における体内動態予測

One of the important therapeutic indices in acute drug poisoning is the prediction of the drug's pharmacokinetic parameters. At present, there are many data available concerning therapeutic drugs and it is possible to search data banks for the pharmacokinetic parameters of these drugs. However, there are few data available on the drugs concerning the pharmacokinetic parameters in acute drug poisoning which occurs when drugs are ingested in extremely large doses. Therefore, to establish an appropriate treatment for acute drug poisoning, the concentrations of benzodiazepines (BZDs) in the sera of patients with acute BZD (triazolam, nitrazepam) poisoning were measured at intervals and the elimination rate constants were estimated. Comparisons of the BZDs concentrations measured and those predicted were made. To assess the elimination rate of BZDs when patients were subjected to hemoperfusion in the management of acute BZD poisoning, the concentrations of BZDs in the sera of patients were measured before and after they underwent direct hemoperfusion. To determine promptly BZDs in serum, a fluorescence polarization immunoassay (FPIA) using a TDx analyzer system (Abbott) was performed and each sample was reexamined and identified by high-performance liquid chromatography (HPLC).Prediction of pharmacokinetic parameters following the rapid determination of drug in the sera of patients with acute drug poisoning is helpful for confirming a clinical diagnosis and selecting an appropriate treatment. this is because the elimination half-life of the drug in a patient who has ingested extremely high doses is not the same as that after ingestion of a therapeutic dose of the drug.

Prediction of the pharmacokinetic parameters of reduced- dolasetron in man using in vitro-in vivo and interspecies allometric scaling

1. Dolasetron (Anzemet®) is apotent andselective 5-HT3 receptor antagonist which is rapidly and extensively reduced to yield its major pharmacologically active metabolite, reduced dolasetron (RD). RD is further metabolized by CYP450 enzymes as well as undergoing renal excretion. As both in vitro and in vivo data on RD were available from animals andman, two approaches to predict the human pharmacokinetic parameters ofRD were assessed. 2. First, in vitro studies, using liver microsomes from animal species and man, were undertaken to measure Vmax and Km and to assess the intrinsic clearance (CLint). With appropriate liver weight and liver bloodflow scaling factorsthe predicted in vivo metabolic clearance (CLm_pred) was calculated. Human CLm_pred was underestimated by a factor of 5 when it was calculated using the above scaling factors. As, in a prospective study, the observed human in vivo metabolic clearance (CLm_obs)is unknown, CLm_pred was substituted into the least-squares correlation equation obtained from a plot of CLm_pred against CLm_obs, using animal data. The estimate of human CLm_obs was improved as it was only underestimated by a factor of 1.5. 3. Second, allometric scaling of in vivo animal pharmacokinetic data, using body weight, was performed topredict pharmacokinetic parameters in man. Good predictions of human pharmacokinetic parameters of RD were obtained for plasma clearance (1.7 l/min predicted versus 1.6 l/min observed), half-life (6.0 h predicted versus 5.6 h observed), and volume of distribution (860.9 l predicted versus 770.4 l observed). 4. The integration of in vitro metabolic data from microsomes gave similar results to conventional allometric scaling, whereas the normalization of clearance by brain weight resulted in an approximately three-fold underestimation of human clearance. 5. For RD, a drug that is eliminated by both renal and metabolic clearance, retrospective conventional allometric scaling allowed accurate prediction of pharmacokinetic parameters in man, whereas in vitro-in vivo scaling resulted in an underestimation of in vivo CLm. Although these results are somewhat at variance, ideally both scaling methods should be applied to improve the prediction of human pharmacokinetic parameters.