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There is an ongoing aim to replace animal and in vitro laboratory models with in silico methods. Such replacement requires the successful validation and comparably good performance of the alternative methods. We have developed an in silico prediction system for human clinical pharmacokinetics, based on machine learning, conformal prediction and a new physiologically-based pharmacokinetic model, i.e. ANDROMEDA. The objectives of this study were: a) to evaluate how well ANDROMEDA predicts the human clinical pharmacokinetics of a previously proposed benchmarking data set comprising 24 physicochemically diverse drugs and 28 small drug molecules new to the market in 2021; b) to compare its predictive performance with that of laboratory methods; and c) to investigate and describe the pharmacokinetic characteristics of the modern drugs. Median and maximum prediction errors for the selected major parameters were ca 1.2 to 2.5-fold and 16-fold for both data sets, respectively. Prediction accuracy was on par with, or better than, the best laboratory-based prediction methods (superior performance for a vast majority of the comparisons), and the prediction range was considerably broader. The modern drugs have higher average molecular weight than those in the benchmarking set from 15 years earlier ( ca 200 g/mol higher), and were predicted to (generally) have relatively complex pharmacokinetics, including permeability and dissolution limitations and significant renal, biliary and/or gut-wall elimination. In conclusion, the results were overall better than those obtained with laboratory methods, and thus serve to further validate the ANDROMEDA in silico system for the prediction of human clinical pharmacokinetics of modern and physicochemically diverse drugs.

The gastrointestinal uptake of macrocyclic compounds is not fully understood. Here we applied our previously validated integrated system based on machine learning and conformal prediction to predict the passive fraction absorbed (f

Challenges/problems forin vitromethodologies for prediction of human clinical pharmacokinetics include inter- and intra-laboratory variability, and common occurance of high limits of quantification, low recovery, low parameter validity and low reproducibility. In this study, 10 drugs with substantial differences in human hepatocyte intrinsic metabolic clearance (CLint) and fraction unbound in plasma (fu) between laboratories were selected. The average and maximum ratios between highest and lowest reported predictedin vivohepatic metabolic clearance (CLH) for the drugs were 529- and 2436-fold, respectively. Thein vivoCLHwas predicted usingin vitroCLintand fudata from the various highly sources and using ourin silicomethodology. The main aim was to compare the predictive accuracies for thein vitroandin silicomethodologies. Prediction errors forin vitromethodology ranged from 1.1-to 578-fold, with an average of 150-fold for lowest predicted estimates and 16-fold for highest predicted estimates. Thein vitrobased predictions produced 36-to 38-fold higher average and maximum prediction errors than thein silicomethodology, respectively. Mean and maximumin silicoprediction errors were 4.2- and 15-fold, respectively, which is consistent with earlier results. In contrast to thein vitromethodology thein silicomodels did not predict high hepatic extraction ratio for drugs with low CLH. Overall, thein silicomethod clearly outperformedin vitrodata for prediction of CLHin man for 10 drugs with large interlaboratory variability.

The ANDROMEDA software, based on machine learning, conformal prediction and a new physiologically-based pharmacokinetic model, was used to predict and characterize the human clinical pharmacokinetics of 30 selected modern small antibiotic compounds (investigational and marketed drugs). A majority of clinical pharmacokinetic data was missing. ANDROMEDA successfully filled this gap. Most antibiotics were predicted and measured to have limited permeability, good metabolic stability and multiple elimination pathways. According to predictions, most of the antibiotics are mainly eliminated renally and biliary and every other antibiotic is mainly eliminated via the renal route. Mean prediction errors for steady state volume of distribution, unbound fraction in plasma, renal and total clearance, oral clearance, fraction absorbed, fraction excreted renally, oral bioavailability and half-life were 1.3- to 2.3-fold. The overall median and maximum prediction errors were 1.5- and 4.8-fold, respectively, and 92 % of predictions had

The ANDROMEDA toolkit for prediction of human clinical pharmacokinetics, based on machine learning, conformal prediction and a new physiologically-based pharmacokinetic model, was used to predict and characterize the human clinical pharmacokinetics of 12 small anticancer drugs marketed in 2021 and 2022 (molecular weight 355 to 1326 g/mol). The study is part of a series of software validations. A majority of clinical pharmacokinetic data was missing. ANDROMEDA successfully filled this gap. Most drugs were predicted/measured to have relatively complex pharmacokinetics, with limited passive permeability+efflux, high degree of plasma protein binding, significant gut-wall elimination and food interaction, biliary excretion and/or limited dissolution potential. Median, mean and maximum prediction errors for steady state volume of distribution, unbound fraction in plasma, blood-to-plasma concentration ratio, hepatic, renal and total clearance, fraction absorbed, oral bioavailability, half-life and degree of food interaction were 1.6-, 2.4- and 17-fold, respectively. Less than 3-fold errors were found for 78 % of predictions. Results are consistent with those obtained in previous validation studies and are better than with the best laboratory-based prediction methods, which validates ANDROMEDA for predictions of human clinical pharmacokinetics of modern small anticancer drugs with multi-mechanistical and challenging pharmacokinetics.

Volume of distribution at steady state (Vss) is an important pharmacokinetic endpoint. In this study we apply machine learning and conformal prediction for human Vss prediction, and make a head-to-head comparison with rat-to-man scaling, allometric scaling and the Rodgers-Lukova method on combined in silico and in vitro data, using a test set of 105 compounds with experimentally observed Vss.The mean prediction error and % with

BackgroundPrevious work has shown considerable laboratory variability of Biopharmaceutics Classification System (BCS) classification, efflux ratio in intestinal cell lines and cytochrome P450 (CYP450)-metabolism pathways. Such variability and inconsistency create uncertainty in predictions of human clinical pharmacokinetics and the pharmacokinetic optimization process and is a problem when developing correspondingin silicomethods.Objectives and MethodologyOne objective of the study was to quantify the degree of laboratory inconsistency for BCS II-classing, MDR-1 and CYP3A4 substrate specificity (substrate/non-substrate). Another objective was to predict BCS II-classing, MDR-1 and CYP3A4 substrate specificity usingin silicomethodology and compare results to laboratory data/classifications.Results and Discussion27 BCS II-classified drugs (with non-contradictory BCS-classing in various sources) were found. 17 (63 %) had anin vivofraction absorbed (fa) of ≥90 % and belong toin vivoBCS I. Within silicomethodology, 74 % correct BCS-classing was reached for the same set of compounds. The mean prediction error for fawas 1.2-fold. MDR-1 and CYP3A4 substrate specificities were collected for 346 and 808 compounds, respectively. For MDR-1, 143 of the compounds had reported data in at least two studies, and out of these, 49 (34 %) and 18 (13 %) had contradictory (reported as both substate and non-substrate) and uncertain substrate specificities, respectively. For CYP3A4, 42 (9.8 %) out of 427 compounds showed inconsistency between laboratories. Within silicomethodology, MDR-1 and CYP3A4 classification predictions were incorrect for 13 and 15 % of compounds.ConclusionThe results show considerable variability/inconsistency for BCS II-classing (63 % inconsistency between BCS II-classing andin vivofa) and MDR-1 (34 % inconsistency between sources) and CYP3A4 (10 % inconsistency between sources) substrate specificities. Corresponding estimates obtained within silicomethodology are 22, 13 and 15 %, respectively, demonstrating the power and applicability of such technology.

IntroductionThe ANDROMEDA software by Prosilico has previously been successfully applied and validated for predictions of absorption characteristics of small drugs in man. The effects of fat food on the gastrointestinal uptake and systemic exposure of drugs have, however, not yet been evaluated with the software.Objective and MethodologyThe main objective was to use ANDROMEDA to predict area under the plasma concentration-time curve ratios in the fed (fat food) and fasted states (AUCfed/AUCfast) for small drugs marketed in 2021 and compare results with corresponding measured clinical estimates. A secondary aim was to make a similar evaluation for a set of selected older small drugs with no and significant food interaction.Results17 new drugs with observed AUCfed/AUCfastbetween 0.4 and 2.0 were found. In addition, 18 older drugs with low (0.5), moderate (1) and high (3.1-5.5) AUCfed/AUCfastwere selected. Predicted AUCfed/AUCfast(for all drugs) had mean, median and maximum errors of 1.4-, 1.1- and 3.9-fold, respectively, and the predictive accuracy (correlation between predicted and observed AUCfed/AUCfast; Q2) was 0.35. For new small drugs, the Q2was 0.41. All 10 new drugs with AUCfed/AUCfastbetween 0.8 and 1.25 were predicted to have AUCfed/AUCfastbetween 0.6 and 1.5 and all 4 new drugs with AUCfed/AUCfastfed/AUCfastfed/AUCfast≥1.25).ConclusionThe results validate ANDROMEDA for prediction of fat food-drug interaction size for small drugs in man. Major advantages with the methodology include that results are produced directly from molecular structure and oral dose and an adequate level of predictive accuracy.

IntroductionSome prodrugs are developed in order to improve gastrointestinal absorption properties such as permeability and solubility/dissolution. Prediction of the uptake of prodrugs and their drugs is challening for reasons including gastrointestinal hydrolysis and active transport.Objective and MethodologyThe objective was to use the ANDROMEDA software by Prosilico to predict absorption characteristics - passive fraction absorbed (fa,passive), dose-adjusted dissolution potential (fdiss) and total fa(fa) - of prodrugs and their drugs (including drugs and their active metabolites), and to evaluate how they differ between prodrugs and drugs and the predictive accuracy of the software.Results70 prodrug-drug pairs were found and selected for the study. The mean predicted fa,passiveand fdissfor the prodrugs were 0.74 and 0.94, respectively. Corresponding estimates for the drugs were 0.72 and 0.98, respectively. For non-hydrolyzed prodrugs, the median relative and absolute prediction errors for fawere 1.17-fold and 0.08, respectively. Corresponding values for drugs were 1.11-fold and 0.07, respectively. The correlation between predicted and observed fafor non-hydrolyzed ester prodrugs and drugs combined (predictive accuracy) was 0.6.ConclusionProdrugs and drugs had similar average predicted fa,passiveand fdiss, and most had or were predicted to have at least 50 % fa. The fafor about 1/3 of non-hydrolyzed prodrugs was higher than for corresponding drugs, showing successful prodrug design. Adequate prediction accuracy validates ANDROMEDA for prediction of prodrug and drug absorption in man.

BackgroundBlood-brain barrier permeability (BBB Pe) and unbound brain-to-plasma concentration ratio (Kp,uu,brain) are relevant parameters describing the brain uptake potential of compounds. BBB efflux by transporter proteins, mainly MDR-1 and BCRP, is an essential factor determining Kp,uu,brain. Kp,uu,brain-values are commonly estimatedin vivoin rats and monkeys and predicted usingin silicomethodology. Such estimates can be used to predict corresponding human clinical values.ObjectiveThe objective of the study was to evaluate the reliability and applicability of human clinical Kp,uu,brain-data for understanding and predictions of brain uptake in man.MethodologyKp,uu,brainin rats, monkeys and humans, measured andin silicopredicted MDR-1 and BCRP substrate specificities andin silicopredicted passive Pewere used for the analysis.In silicopredictions were done using the ANDROMEDA by Prosilico ADME/PK-prediction software.Results and DiscussionRat and monkey Kp,uu,brain-values were highly correlated (R^2=0.74; n=17). Based on this finding a correlation between rat and human Kp,uu,brainwas expected. However, no correlation between rat and human Kp,uu,brainwas found (R^2=0.01; n=13). There was no (as also anticipated) correlation between passive Peand human Kp,uu,brain(R^2=0.04; n=16) and compounds with measured or predicted efflux did not have lower Kp,uu,brainthan compounds without efflux. The compound with highest Kp,uu,brainin man (2.8) is effluxed and predicted to have high passive Peand has no apparent efflux at the rat BBB. The MDR-1 substrate with highest Kp,uu,brainin rat (2.4) has very low Kp,uu,brainin man (0.15) is predicted to have high passive Pe.ConclusionResults indicate that available human Kp,uu,brain-data are too uncertain to be applicable for validation of predictions and understanding of clinical brain uptake of drugs and drug candidates.

IntroductionConformal prediction (CP) methodology sits on top of machine learning methods and produces prediction confidence intervals that depend on how “strange” (non-conforming) test compounds are compared to training set compounds. CP has previously been successfully applied for prediction of steady-state volume of distribution (Vss) in humans, with 69 % of observations within the prediction interval at a 70 % confidence level. We have developed CP models for a variety of human pharmacokinetic (PK) parameters and validated their predictive accuracy (predictedvsobserved estimates), but not validated prediction confidence intervals for them. The main objective of this study was to predict 70 % confidence intervals for Vss, unbound fraction in plasma (fu), intrinsic metabolic clearance (CLint), fraction absorbed passively (fa,passive) and maximum fraction dissolved (fdiss) for a variety of compounds in man and investigate the consistency between prediction intervals and observed/measured values.MethodologyCP models featured in the ANDROMEDA software by Prosilico were used for prediction of 70 % confidence intervals of Vss, fu, CLint, fa,passiveand fdissfor compounds from different chemical classes and with broad physicochemical variety and for small drugs marketed in 2021.Results70 % prediction confidence intervals for 217, 117, 117, 89 and 89 compounds were produced for Vss, fu, CLint, fa,passiveand fdiss, respectively. 78 % (expected 70 %) of observed data were within 70 % confidence intervals for the parameters. 70 % of predictions of Vss, fu, CLintfa,passiveand fdissare expected to have errors of maximally 2-, 4- and 6-fold and 7 and 12 %, respectively, which is in line with prediction errors. These findings validate the CP methodology.ConclusionIn conclusion, the results further validate CP models and confidence intervals of ANDROMEDA for prediction of human PK.

Variability of the unbound fraction in plasma (f(u)) between labs, methods and conditions is known to exist. Variability and uncertainty of this parameter influence predictions of the overall pharmacokinetics of drug candidates and might jeopardise safety in early clinical trials. Objectives of this study were to evaluate the variability of human in vitro f(u)-estimates between labs for a range of different drugs, and to develop and validate an in silico f(u)-prediction method and compare the results to the lab variability. A new in silico method with prediction accuracy (Q(2)) of 0.69 for log f(u) was developed. The median and maximum prediction errors were 1.9- and 92-fold, respectively. Corresponding estimates for lab variability (ratio between max and min f(u) for each compound) were 2.0- and 185-fold, respectively. Greater than 10-fold lab variability was found for 14 of 117 selected compounds. Comparisons demonstrate that in silico predictions were about as reliable as lab estimates when these have been generated during different conditions. Results propose that the new validated in silico prediction method is valuable not only for predictions at the drug design stage, but also for reducing uncertainties of f(u)-estimations and improving safety of drug candidates entering the clinical phase.

IntroductionIn vitromeasurements and predictions of human clinical pharmacokinetics (PK) are sometimes hindered and made impossible due to factors such as extensive binding to materials, low methodological sensitivity and large variability.MethodsThe objective was to find compounds out of reach forin vitroPK-methods and (if possible) predict corresponding human clinical estimates using the ANDROMEDA by Prosilico software.In vitromethods selected for the investigation were human microsomes and hepatocytes for measuring and predicting intrinsic hepatic metabolic clearance (CLint), Caco-2 cells for measuring apparent intestinal permeability (Papp) for prediction of fraction absorbed (fa), plasma for measurement and estimation of unbound fraction (fu), and water and buffers for measuring solubility (S) for prediction ofin vivodissolution potential (fdiss).Results and Conclusion73 non-quantifiablein vitroPK-measurements for 70 compounds were found in the literature: 40 for CLint, 8 for Papp, 11 for fuand 14 for S. ANDROMEDA was successful in predicting all corresponding clinical PK-estimates for the selection of compounds with non-quantifiablein vitroPK, and predicted estimates were generally in line with observedin vivodata and results/problems atin vitrolaboratories. Thus, ANDROMEDA is applicable for predicting human clinical PK for compounds out of reach for laboratory methods.

BackgroundPassive blood-brain barrier permeability (BBB Pe), fraction bound to brain tissue (fb,brain) and efflux by transport proteins MDR-1 and BCRP are essential determinants for the brain uptake and disposition of drugs.MethodsThe main objective of the study was to use the software ANDROMEDA by Prosilico to predict passive BBB Pe- and fb,brain-classes and MDR-1- and BCRP-specificities for various classes of antidepressants and for CNS-active small drugs marketed during 2020 and 2021, and then to position them according to a new 2-dimensional Brainavailability-Matrix (8 passive BBB Pex 4 fb,brainclasses, where class 11 has highest and 84 lowest values/brainavailability). Predicted estimates were used, except for cases where measured values were available.Results and ConclusionResults for 53 drugs show that adequate CNS uptake and disposition are achieved for compounds placed in the zones for low, moderate and high brainavailability, despite efflux. They also show that high brainavailability and efflux are common for CNS-active drugs and that modern CNS-active drugs generally have lower brainavailability than older antidepressive drugs. Furthermore, they demonstrate that ANDROMEDA by Prosilico and the new Brainavailability-Matrix are applicable for prediction, optimization and positioning of CNS uptake and disposition of drugs and drug candidates in man.

IntroductionVarious in vitro methods are used to measure absorption, distribution, metabolism and excretion/pharmacokinetics (ADME/PK) of candidate drugs and predict and decide whether properties are clinically adequate.MethodsObjectives were to evaluate variability within and between laboratories for commonly used human in vitro ADME/PK methods and to explore whether reliable thresholds may be defined. The literature was searched for in vitro data for intrinsic metabolic clearance (hepatocyte CLint), apparent intestinal permeability (Caco-2 Papp), efflux ratio (Caco-2 ER), solubility (S) and BCS-class, and corresponding clinical estimates. In vitro ADME/PK data for three example drugs (atenolol, diclofenac and gemfibrozil) were used to predict human in vivo ADME/PK and investigate whether these would pass a compound selection process.Results and ConclusionsInterlaboratory variability is considerable, especially for fu, S, ER and BCS-classification, and on average about twice as high as intralaboratory variability. Approximate mean interlaboratory variability for CLint, Papp, ER and fu (3- to 3.5-fold) appears to be about 2- to 3-fold higher than corresponding interlaboratory variability. Mean and maximum interlaboratory range for CLint, Papp, ER, fu and S are approximately 5- to 100-fold and 50- to 4500-fold, respectively, with second largest range for fu and largest range for S. For one drug, laboratories produced almost 1000-fold different CLint • fu-values. It appears difficult/impossible to set clear clinically useful thresholds, especially for CLint, ER and S. Poor in vitro-in vivo consistency for S and BCS-classification and large portions of compounds out of reach for Caco-2 and conventional hepatocyte assays are evident. Predictions for reference compounds are consistent with inadequate in vivo ADME/PK. Ways to improve predictions and compound selection are suggested.

BackgroundA shift towards higher molecular weight (MW) of drug candidates is anticipated to lead to changed pharmacokinetics (PK), including deteriorated absorption.MethodsThe objective of the study was to investigate changes in MW and PK of drugs over time by comparing MW and measured PK of small drugs (here MWin silicopredicted (data produced using the ANDROMEDA by Prosilico software) and of small drugs marketed in 2021 (n=28).ResultsApparently, there has been a shift towards higher MW (from 355 to 551 g/mole on average). This has influenced PK-parameters such as unbound fraction (on average approximately halved), fraction excreted renally (on average approximately halved; markedly decreased contribution by active secretion), bile excretion (almost 4-fold increased appearance; now for more than every other drug) and intrinsic metabolic clearance (increased). The very high percentage of modern drugs with (according toin silicopredictions) significant renal and biliary excretion and gut-wall extraction, metabolic stability, limited passive intestinal permeability+efflux, limited gastrointestinal dissolution/solubility potential and/or a very low fuincreases complexity in predictions and places demands on predictive laboratory and computational methods.ConclusionIncreased MW and changed PK-profiles (increased complexity) with time were observed. This shows the need for updating method set-ups for quantification and prediction of PK-parameters. ANDROMEDA has the capability to predict and optimize human clinical PK-characteristics of modern drug candidates with high accuracy.

BackgroundPharmacokinetics (PK), exposure profiles and doses of candidate drugs in man are commonly predicted using data produced using various in vitro methods, such as hepatocytes (for intrinsic metabolic clearance (CLint)), plasma (for unbound fraction (fu)), Caco-2 (measuring apparent permeability (Papp) for prediction of in vivo fraction absorbed (fa)) and plasma water and buffers (measuring solubility (S) for prediction of in vivo fraction dissolved (fdiss)). For best possible predictions it is required that the clinical relevance of in vitro data is understood (in vitro-in vivo relationships) and that uncertainties have been investigated and considered.MethodsThe aim was to investigate in vitro-in vivo relationships for CLint, Pappvs fa and S vs fdiss and interlaboratory variability for fu, describe the clinical significance and uncertainties at certain levels of in vitro CLint, fu, Papp and S, and (based on the findings) develop a general in vitro-in vivo translation guide.Results and ConclusionIt was possible to finf data for describing how in vivo CLint, fa and fdiss distribute and varies at different levels of in vitro CLint, Papp and S and how fu varies between laboratories and methods at different fu-levels. It is apparent that there are considerable interlaboratory variabilities for CLint, fu and Papp: corresponding to up to 2500-, 700- and 35-fold variability for CLint, fu and fa, respectively. Apparently, S is a poor predictor of fdiss. Proposed S-thresholds do not seem clinically useful (overestimated). It does not seem appropriate to define in vitro CLint of 0.5-2 µL/min/106 cells as good metabolic stability (rather moderate to moderately high). Results shown for CLint, Papp and fu are applicable as general guidelines when internal standard values for reference compounds are unavailable.

BackgroundPROTACs are comparably large and flexible compounds with limited solubility (S) and permeability (Pe). It is crucial to better understand, predict and optimize their human clinical pharmacokinetics (PK).MethodsThe main objective was to use the ANDROMEDA by Prosilico software to predict the human clinicalin vivodissolution potential (fdiss) and fraction absorbed (fa) of 23 PROTACs at a dose level of 50 mg and to explore whether there is any relationship betweenin vitroS andin silicopredictedin vivofdiss.ResultsIn silicopredictions showed that the PROTACs are effluxed by intestinal transporters and have limited fdiss(34 to 98 %), permeability and fa(13 to 58 %) in man. For some PROTACs this may be a major obstacle and jeopardize the clinical development programs, especially in cases of required high oral dose. A modest relationship betweenin vitroS and predictedin vivofdisswas demonstrated (R2=0.26). Predicted human fa(27 %) and oral bioavailability (20 %) of ARV-110 (a PROTAC with some availablein vivoPK data in rodents and man) were consistent with data obtained in rodents (estimated faapproximately 30-40 %; measured oral bioavailability 27-38 %). Laboratories were unable to quantify S for 7 (30 %) of the PROTACs. In contrast, ANDROMEDA could predict parameters for all.ConclusionANDROMEDA predicted fdissand fafor all the chosen PROTACs and showed limited fdiss, Peand faand dose-dependent fdissand fa. One available example shows promise for the applicability of ANDROMEDA for predicting biopharmaceutics of PROTACsin vivoin man. Weak to modest correlations between S and fdissand a considerable portion of compounds with non-quantifiable S limit the use of S-data to predict the uptake of PROTACs.

BackgroundIt is important that pharmacokinetic (PK) prediction methods are validated, and also for compounds with varying physicochemical properties, molecular weights and PK characteristics.MethodsThe objective was to investigate how well the ANDROMEDA by Prosilico software predicts the clinical PK of four compounds of natural origin and with PK obstacles, not yet fully characterized PK, and/or inaccurate lab method-based predictions - colistin (negligible absorption, good metabolic stability, significant excretion), curucumin (low solubility, apparently poor bioavailability), UCN-01 (extremely high degree of plasma protein binding, metabolic stability, long half-life and poor PK prediction) and voclosporin (poorly understood PK).ResultsAll categorial predictions except one were correct, and the median prediction error was 2.5-fold. Largest prediction errors were found for the unbound fraction in plasma (>24-fold), clearance (178-fold) and half-life (90-fold) of UCN-01. Corresponding errors for clearance and half-life obtained with allometry were greater, 5800- and 145-fold, respectively. Extremely high affinity for alpha1-acid glycoprotein could explain these large prediction errors for this compound. A substantial amount of data and knowledge was added with the predictions.ConclusionDespite challenging compounds and PK, predictions were comparably good. The results further validated ANDROMEDA by Prosilico for human clinical PK-predictions.

In vitro-in vivo prediction results for hepatic metabolic clearance (CL

Pharmacokinetic/toxicokinetic (PK/TK) information for chemicals in humans is generally lacking. Here we applied machine learning, conformal prediction and a new physiologically-based PK/TK model for prediction of the human PK/TK of 65 chemicals from different classes, including carcinogens, food constituents and preservatives, vitamins, sweeteners, dyes and colours, pesticides, alternative medicines, flame retardants, psychoactive drugs, dioxins, poisons, UV-absorbents, surfactants, solvents and cosmetics.About 80% of the main human PK/TK (fraction absorbed, oral bioavailability, half-life, unbound fraction in plasma, clearance, volume of distribution, fraction excreted) for the selected chemicals was missing in the literature. This information was now added (from in silico predictions). Median and mean prediction errors for these parameters were 1.3- to 2.7-fold and 1.4- to 4.8-fold, respectively. In total, 59 and 86% of predictions had errors in silico predictions). Median and mean prediction errors for these parameters were 1.3- to 2.7-fold and 1.4- to 4.8-fold, respectively. In total, 59 and 86% of predictions had errors

Variability of the unbound fraction in plasma (f

Well-established techniques are available to predict in vivo hepatic uptake and metabolism from in vitro data, but predictive models for biliary clearance remain elusive. Several studies have verified the expression and activity of ATP-binding cassette (ABC) efflux transporters central to biliary clearance in freshly isolated rat hepatocytes, raising the possibility of predicting biliary clearance from in vitro efflux measurements. In the present study, short-term plated rat hepatocytes were evaluated as a model to predict biliary clearance from in vitro efflux measurements before major changes in transporter expression known to take place in long-term hepatocyte cultures. The short-term cultures were carefully characterized for their uptake and metabolic properties using a set of model compounds. In vitro efflux was studied using digoxin, fexofenadine, napsagatran, and rosuvastatin, representing compounds with over 100-fold differences in efflux rates in vitro and 60-fold difference in measured in vivo biliary clearance. The predicted biliary clearances from short-term plated rat hepatocytes were within 2-fold of measured in vivo values. As in vitro efflux includes both basolateral and canalicular effluxes, pronounced basolateral efflux may introduce errors in predictions for some compounds. In addition, in vitro rat hepatocyte uptake rates corrected for simultaneous efflux predicted rat in vivo hepatic clearance of the biliary cleared compounds with less than 2-fold error. Short-term plated hepatocytes could thus be used to quantify hepatocyte uptake, metabolism, and efflux of compounds and considerably improve the prediction of hepatic clearance, especially for compounds with a large biliary clearance component.

Machine learning has recently become popular and much used within the life science research domain, e.g., for finding quantitative structure-activity relationships (QSARs) between molecular structures and different biological end points. In the work presented here, we have applied orthogonal partial least-squares (OPLS), principal component analysis (PCA), and random forests (RF) methods for classification as well as regression analysis to a publicly available in vivo data set in order to assess the intrinsic metabolic clearance (CL(int)) in humans. The derived classification models are able to identify compounds with CL(int) lower and higher than 1500 mL/min, respectively, with nearly 80% accuracy. The most relevant descriptors are of lipophilicity and charge/polarizability types. Furthermore, the accuracy from a classification model based on regression analysis, using the 1500 mL/min cutoff, is also around 80%. These results suggest the usefulness of machine learning techniques to derive robust and predictive models in the area of in vivo ADMET (absorption, distribution, metabolism, elimination, and toxicity) modeling.

Permeability (Pe) is one of the key determinants in the absorption, distribution, metabolism, excretion/pharmacokinetics (ADME/PK) of drugs and their metabolites. Predictions of ADME/PK, interactions, elimination routes, exposures, and toxicity require, therefore, that the role of permeability in different organs is considered, investigated and understood. That includes studies of and knowledge about the relation between Pe and fraction absorbed (fa) (or fraction reabsorbed; fra) in various organs, and the interplay between passive permeability and active permeability, metabolism and solubility/dissolution. Relationships between passive Pe and fa in the human intestine, liver, renal tubuli (fra), and brain have been established, and these are the basis of the Permeability-Based Classification System (PCS). This system demonstrates sigmoidal Pe versus fa and fra relationships of different shapes and shifts, and is divided into four permeability categories (very high/high/intermediate/low). Results show or indicate that the liver and brain have comparably high intrinsic passive uptake capacities, metabolism (rather than uptake, diffusion, and dissociation) is the general rate-limiting step in hepatic metabolic clearance (CLH), and few high permeability compounds have dissolution-limited gastrointestinal fa. Active transport processes contribute to the intestinal and hepatic uptake, and renal, biliary, and intestinal drug excretion, of many drugs with limited passive Pe. Active transport could be clinically relevant for brain uptake of both low and high passive permeability compounds. Related drug–drug interactions and polymorphism appear most pronounced for drugs actively absorbed and excreted by the liver. Combined with intrinsic metabolic CL data, the PCS is useful for predictions of CLH, renal and biliary excretion potential, gut-wall extraction ratio, oral bioavailability and effects of polymorphism, and for assessment of potential drug–drug or drug–metabolite interactions, and drug and metabolite organ/cell trapping. Keywords: absorption; classification system; clearance; excretion; interaction; metabolism; permeability; pharmacokinetics; prediction; toxicity

The blood-brain barrier is often perceived as relatively impermeable, and various cut-off values for zero or limited brain permeability have been suggested. The validity of these values has been evaluated in this review. The barrier appears to have a very high permeability and absorptive capacity: sufficient to absorb compounds with polar surface area270 A(2), molecular weight1,000 Da, log D-3.5 and equilibrium brain-to-blood concentration ratio0.01 well. Sufficient intestinal uptake indicates good passive brain uptake potential. The uptake is potentially more sensitive to involvement and changes of active transport than in the intestines. A physiologically based in vitro-in vivo method for prediction of brain uptake is presented.

Permeability (Pe) and solubility/dissolution are two major determinants of gastrointestinal (GI) drug absorption. Good prediction of these is crucial for predicting doses, exposures and potential interactions, and for selecting appropriate candidate drugs. The main objective was to evaluate screening methods for prediction of GI Pe, solubility/dissolution and fraction absorbed (fa) in humans. The most accurate Pe models for prediction of fa of passively transported and highly soluble compounds appear to be the 2/4/A1 rat small intestinal cell model (in-vitro and in-silico), a newly developed artificial-membrane method, and a semi-empirical approach based on in-vitro membrane affinity to immobilized lipid bilayers, effective molecular weight and physiological GI variables. The predictability of in-vitro Caco-2, in-situ perfusion and other artificial membrane methods seems comparably low. The Pe and fa in humans for compounds that undergo mainly active transport were predicted poorly by all models investigated. However, the rat in-situ perfusion model appears useful for prediction of active uptake potential (complete active uptake is generally well predicted), and Caco-2 cells are useful for studying bidirectional active transport, respectively. Human intestinal in-vitro Pe, which correlates well with fa for passively transported compounds, could possibly also have potential to improve/enable predictions of fa for actively transported substances. Molecular descriptor data could give an indication of the passive absorption potential. The ‘maximum absorbable dose’ and ‘dose number’ approaches, and solubility/dissolution data obtained in aqueous media, appear to underestimate in-vivo dissolution to a considerable extent. Predictions of in-vivo dissolution should preferably be done from in-vitro dissolution data obtained using either real or validated simulated GI fluids.

The mechanisms controlling rates and routes for intestinal absorption of nutrients and small compounds are still not fully clarified. In the present study we aimed to investigate the effect of solvent drag on intestinal permeability of compounds with different molecular sizes in humans and rats. The effective intestinal permeabilities (P eff ) of hydrophilic compounds (MW 60-4000) were determined in the single-pass perfused jejunum in humans in vivo and rats in situ under iso- and hypotonic conditions. The transport mechanism(s) of water and the importance of the solvent drag effect were investigated by the use of D 2 O. This is the first report in humans establishing the relation between in vivo measured jejunal P eff and molecular size for hydrophilic compounds. In addition, in rats we also found a molecular-size selectivity for hydrophilic compounds similar to man. The jejunal P eff of water and urea (MW 60) in both species were several times higher than predicted from their physicochemical properties. In humans, the jejunal absorption of urea and creatinine (MW 113) was increased by solvent drag, while no effect was found for the other investigated compounds. In rats, P eff for urea and creatinine were unaffected. In conclusion, it is still unclear if solvent drag occurs mainly through the para- or transcellular route, although, results from this study further add to our earlier reports suggesting that the transcellular route is most important from a quantitative point of view regardless of physicochemical properties of the transported compounds.

The goal of this study was to determine the most appropriate hydrodynamic model for the Loc-I-Gut in-vivo perfusion system. The general mixing-tank-in-series model, which can approximate single mixing tank and laminar and plug-flow hydrodynamics, was fitted to the observed experimental residence-time distribution curves for the non-absorbable marker [14C]PEG 4000. The residence-time distribution analysis shows that the hydrodynamics of the perfusion solution within the jejunal segment in man is well approximated by a model containing on average between 1–2 mixing tanks in series. The solution is well mixed when using perfusion rates of 20, 30 and 60 mL min−. The average mean residence time estimates from the fitted residence-time distribution were 12 ± 7.6, 15 ± 4.2 and 7.7 ± 4.6 min, respectively, at these three perfusion rates. The mean volumes of the segment (Vs) were 25 ± 15, 45 ± 12 and 46 ± 27 mL, respectively. There were no statistical differences between 20, 30 and 60 mL min− in respect of the number of mixing tanks (n) and mean residence times. This residence-time distribution analysis indicates that the luminal fluid in the Loc-I-Gut perfusion system is well-mixed, and that permeability calculations based on the well-mixed assumption most closely approximate the actual local (average) membrane permeability within the perfused segment.

Purpose. Our main aim is to determine the effective intestinal permeability (Peff) in the rat jejunum in situ for 10 compounds with different absorption mechanisms and a broad range of physico chemical properties, and then compare them with corresponding historical human in vivo Peff values. Methods. The rat Peff coefficients are determined using an in situ perfusion model in anaesthetized animals. The perfusion flow rate used is 0.2 ml/min, which is 10 times lower than that used in humans. The viability of the method is assessed by testing the physiological function of the rat intestine during perfusions. Results. The Peff for passively absorbed compounds is on average 3.6 times higher in humans compared to rats (Peff,man = 3.6 · Peff,rat + 0.03·10−4; R^2 = 1.00). Solutes with carrier-mediated absorption deviate from this relationship, which indicates that an absolute scaling of active processes from animal to man is difficult, and therefore needs further investigation. The fraction absorbed of drugs after oral administration in humans (fa) can be estimated from 1 − e−(2·P eff,man ·t res /r·2.8). Conclusions. Rat and human jejunum Peff estimates of passively absorbed solutes correlate highly, and both can be used with precision to predict in vivo oral absorption in man. The carrier-mediated transport requires scaling between the models, since the transport maximum and/or substrate specificity might differ. Finally, we emphasize the absolute necessity of including marker compounds for continuous monitoring of intestinal viability.

1. We applied the regression offset approach to predict rat in vivo intrinsic clearance (CLint) for 54 new chemical acid entities with high plasma protein binding values and low renal clearance (CL). The prediction success was correlated to volume of distribution (Vd), molecular weight (Mw) and CL. 2. A correlation between poor in vitro-in vivo extrapolation (IVIVE) and Vd values distinct from the Vd of albumin (0.1-0.2 L/kg) was revealed. For compounds with a Vd value above 0.5 L/kg, 0% of the predictions of in vivo CLint was within twofold of the observed value, compared to 69% for compounds with a Vd value below 0.5 L/kg. 3. Compounds with a Mw below 450 g/mol demonstrated more accurate in vivo CLint predictions than compounds with a Mw above 450 g/mol, i.e. 63% compared to 21% within twofold. For compounds with in vivo CL below 30% of the liver blood flow (LBF), 53% of the predictions was within twofold of the observed value, compared to 0% for compounds with CL above 30% of the LBF. 4. We show that accurate IVIVE for acidic compounds with high plasma protein binding and low renal CL can be associated with a low Vd (i.e. around the Vd of albumin) and with a low in vivo CL, and that Mw is an important optimization parameter for pharmacokinetic. This study also further demonstrates the advantages of the application of the regression method for identifying cases when metabolic CL is not the single major elimination pathway.

EXPERIMENTAL ESTIMATION OF THE EFFECTIVE UNSTIRRED WATER LAYER THICKNESS IN THE HUMAN JEJUNUM, AND ITS IMPORTANCE IN ORAL-DRUG ABSORPTION

Food ingestion can influence the absorption of levodopa in the intestine and thereby contribute to fluctuations of motor functions in Parkinson patients. Obstruction of the active transport of levodopa by amino acids can be one factor. Paracellular drug absorption, a route proposed to be influenced by net transport of water across the intestinal epithelium, might occur for a small and hydrophilic drug such as levodopa. In the present study we studied how luminal L-leucine (60 mmol/L), alone or combined with hypotonicity, might stimulate net water absorption, and levodopa uptake in the human small intestine, since this possibly can contribute to the variable intestinal absorption of levodopa. The Loc-I-Gut perfusion technique was used in 10 healthy volunteers to study the effects of induced net fluid absorption on the small intestinal absorption of levodopa (2.5 mmol/L). An induced net fluid absorption was observed only when L-leucine was combined with a hypoosmolar perfusion solution. However, this did not enhance the intestinal permeability of levodopa. In conclusion, we suggest that the variability in the absorption of levodopa in Parkinson’s disease cannot be explained by differences in transmucosal water flux in the human small intestine.

The main objectives were to investigate the roles of and interplay between determinants of hepatic clearance (CL(H)) in humans, to develop a methodology and reference system for the evaluation and prediction of the rate-limiting step in CL(H), and to update the dispersion model and compare it with traditionally used liver extraction models. The new methodology enables predictions of the hepatic uptake and CL(H), dissociation, and rate-limiting step. In general, absorption, dissociation and diffusion are comparably rapid processes, and metabolism is rate-limiting. The liver appears to have a high passive uptake capacity. The Modified Dispersion Model (MDM) has a dispersion number of 0.5 and a distribution factor (df = 0.87) for the correction of a longer hepatic transit time of unbound molecules and the exclusion of the hidden unbound fraction within erythrocytes. Liver models are functionally equivalent at low CL(H), but differ for highly extracted compounds. Well-stirred and parallel-tube models demonstrate the greatest difference in performance, for example, 6- and 800,000-fold differences in the estimated in vivo intrinsic CL(H) and predicted oral bioavailability of the high CL(H) drug naloxone, respectively. The roles of and interplay between determinants of CL(H) have been further clarified and can now be better predicted. Apparent advantages with the MDM include its scientific rationale and intermediate/ balanced performance.

The main objective was to evaluate and propose methods for predicting biliary clearance (CLbile) and enterohepatic circulation (EHC) of intact drugs in man. Another aim was to evaluate to role of intestinal drug secretion and propose a method for prediction of intestinal secretion CL (CLi). Animal data poorly predict the CL and CLbile of biliary excreted drugs, and the suggested molecular weight threshold for bile excretion as the dominant elimination route does not seem to hold. Active transport, low metabolic intrinsic CL (CLint) and, as an approximation, permeability (Pe) less than that of metoprolol is required for substantial CLbile to occur. The typical EHC plasma concentration vs time profile (multiple peaks) is demonstrated for many low metabolic CLint-compounds with efflux and moderate to high intestinal Pe and fraction absorbed. Physiologically-based in-vitro to in-vivo (PB-IVIV) methodology with in-vitro intrinsic CLbile-data obtained with sandwich-cultured human hepatocytes has generated 2- and 5-fold underpredictions for two compounds with intermediate to high CLbile. This is despite not considering the unbound fraction. Possible explanations include low transporter activity and diffusion limitations in the in-vitro experiments. Intestinal reabsorption and EHC were also neglected in these predictions and in-vivo CLbile estimations. The sandwich model and these reference data are still very useful. Consideration of an empirical scaling factor and a newly developed approach that accounts for intestinal reabsorption and EHC could potentially lead to improved PB-IVIV predictions of CLbile. Apparently, no attempts have been made to predict CLi. Elimination via the intestinal route does not appear to be of great importance for the few compounds with available data, but could be equally as important as bile excretion. Net secretion in-vitro Pe and newly estimated in-vivo intrinsic CLi data for digoxin and rosuvastatin could be useful for approximation of CLi of other compounds.

The kidneys have the capability to both excrete and metabolise drugs. An understanding of mechanisms that determine these processes is required for the prediction of pharmacokinetics, exposures, doses and interactions of candidate drugs. This is particularly important for compounds predicted to have low or negligible non-renal clearance (CL). Clinically significant interactions in drug transport occur mostly in the kidneys. The main objective was to evaluate methods for prediction of excretion and metabolic renal CL (CLR) in humans. CLR is difficult to predict because of the involvement of bi-directional passive and active tubular transport, differences in uptake capacity, pH and residence time on luminal and blood sides of tubular cells, and limited knowledge about regional tubular residence time, permeability (Pe) and metabolic capacity. Allometry provides poor predictions of excretion CLR because of species differences in unbound fraction, urine pH and active transport. The correlation between fraction excreted unchanged in urine (fe) in humans and animals is also poor, except for compounds with high passive Pe (extensive/complete tubular reabsorption; zero/negligible fe) and/or high non-renal CL. Physiologically based in-vitro/in-vivo methods could potentially be useful for predicting CLR. Filtration could easily be predicted. Prediction of tubular secretion CL requires an in-vitro transport model and establishment of an in-vitro/in-vivo relationship, and does not appear to have been attempted. The relationship between passive Pe and tubular fraction reabsorbed (freabs) for compounds with and without apparent secretion has recently been established and useful equations and limits for prediction were developed. The suggestion that reabsorption has a lipophilicity cut-off does not seem to hold. Instead, compounds with passive Pe that is less than or equal to that of atenolol are expected to have negligible passive freabs. Compounds with passive Pe that is equal to or higher than that of carbamazepine are expected to have complete freabs. For compounds with intermediate Pe the relationship is irregular and freabs is difficult to predict. Tubular cells are comparably impermeable (for passive diffusion), and show regional differences in enzymatic and transporter activities. This limits the usefulness of microsome data and makes microsome-based predictions of metabolic CLR questionable. Renal concentrations and activities of CYP450s are comparably low, suggesting that CYP450 substrates have negligible metabolic CLR. The metabolic CLR of high-Pe UDP-glucuronyltransferase substrates could contribute to the total CL.

Intestinal mucosal cells operate with different metabolic and transport activity, and not all of them are involved in drug absorption and metabolism. The fraction of these cells involved is dependent on the absorption characteristics of compounds and is difficult to predict (it is probably small). The cells also appear comparably impermeable. This shows a limited applicability of microsome intrinsic clearance (CLint)-data for prediction of gut-wall metabolism, and the difficulty to predict the gut-wall CL (CLGW) and extraction ratio (EGW). The objectives of this review were to evaluate determinants and methods for prediction of first-pass and systemic EGW and CLGW in man, and if required and possible, develop new simple prediction methodology. Animal gut-wall metabolism data do not appear reliable for scaling to man. In general, the systemic CLGW is low compared with the hepatic CL. For a moderately extracted CYP3A4-substrate with high permeability, midazolam, the gut-wall/hepatic CL-ratio is only 1/35. This suggests (as a general rule) that systemic CLGW can be neglected when predicting the total CL. First-pass EGW could be of importance, especially for substrates of CYP3A4 and conjugating enzymes. For several reasons, including those presented above and that blood flow based models are not applicable in the absorptive direction, it seems poorly predicted with available methodology. Prediction errors are large (several-fold on average; maximum-15-fold). A new simple first-pass EGW-prediction method that compensates for regional and local differences in absorption and metabolic activity has been developed. It has been based on human cell in-vitro CLint and fractional absorption from the small intestine for reference (including verapamil) and test substances, and in-vivo first-pass EGW-data for reference substances. First-pass EGW-values for CYP3A4-substrates with various degrees of gastrointestinal uptake and CLint and a CYP2D6-substrate were well-predicted (negligible errors). More high quality in-vitro CLint- and in-vivo EGW-data are required for further validation of the method.

Physiologically based methods generally perform poorly in predicting in-vivo hepatic CL (CLH) from intrinsic clearance (CLint) in microsomes in-vitro and unbound fraction in blood (fu,bl). Various strategies to improve the predictability have been developed, and inclusion of an empirical scaling factor (SF) seems to give the best results. This investigation was undertaken to evaluate this methodology and to find ways to improve it further. The work was based on a diverse data set taken from Ito and Houston (2005). Another objective was to evaluate whether rationalization of CLH predictions can be made by replacing blood/plasma-concentration ratio (Cbl/Cpl) measurements with SFs. There were apparently no or weak correlations between prediction errors and lipophilicity, permeability (compounds with low permeability missing in the data set) and main metabolizing CYP450s. The use of CLint class (high/low) and drug class (acid/base/neutral) SFs (the CD-SF method) gives improved and reasonable predictions: 1.3-fold median error (an accurate prediction has a 1-fold error), 76% within 2-fold-error, and a median absolute rank ordering error of 2 for CLH (n = 29). This approach is better than the method with a single SF. Mean (P < 0.05) and median errors, fraction within certain error ranges, higher percentage with most accurate predictions, and ranking were all better, and 76% of predictions were more accurate with this new method. Results are particularly good for bases, which generally have higher CLH and the potential to be incorrectly selected/rejected as candidate drugs. Reasonable predictions of fu,bl can be made from plasma fu (fu,pl) and empirical blood cell binding SFs (B-SFs; 1 for low fu,pl acids; 0.62 for other substances). Mean and median fu,bl prediction errors are negligible. The use of the CD-SF method with predicted fu,bl (the BCD-SF method) also gives improved and reasonable results (1.4-fold median error; 66% within 2-fold-error; median absolute rank ordering error = 1). This new empirical approach seems sufficiently good for use during the early screening; it gives reasonable estimates of CLH and good ranking, which allows replacement of Cbl/Cpl measurements by a simple equation.

This review has evaluated the Biopharmaceutics Classification System (BCS) and improvements have been proposed. The BCS has a very strict solubility/dissolution limit, a generous Pe-limit (≥ 14-times higher rate constant limit for dissolution than for permeation), and is stricter for drugs with a long half-life (t1/2). Available human in-vivo, in-vitro, and in-silico Pe-methods cannot classify Pe for moderately to highly permeable substances sufficiently well, and in-vitro data often underpredict the in-vivo dissolution potential and rate. Good in-vivo dissolution and absorption can be expected for most high Pe drug products. It has not been possible to find a highly permeable product with a Dose number (Do) < 385 (< 2400 in the fed state) that is clearly incompletely absorbed, and near complete uptake has been shown for a drug product with a Do of 660000. The potential implication of these findings is that many true BCS Class I drug products are incorrectly classified. This could be a reason for the limited use of this system. On this basis, it has been suggested that: the limit for high for solubility/dissolution is decreased (to >40 and >95% dissolved within 30min and 3 h, respectively); the limit for high Pe is increased (to >Pe of metoprolol); accurate Pe-models or in-vivo fraction absorbed data are used; solubility/dissolution tests are performed using real or validated simulated gastrointestinal fluids; in-vitro/in-vivo dissolution relationships are established; the t½ is considered; and the rate-limiting step for in-vivo absorption is determined. A major change could be to reduce the BCS into two classes: permeation-rate (Class I) or dissolution-rate (Class II) limited absorption. It is believed that this could give a better balance and increase the number of biowaivers.

The objective was to establish in vitro passive permeability (P e) vs in vivo fraction absorbed (f a)-relationships for each passage through the human intestine, liver, renal tubuli and brain, and develop a P e-based ADME/PK classification system (PCS). P e- and intestinal f a-data were taken from an available data set. Hepatic f a was calculated based on extraction ratios of the unbound fraction of drugs (with support from animal in vivo uptake data). Renal f a (reabsorption) was estimated using renal pharmacokinetic data, and brain f a was predicted using animal in vitro and in vivo brain P e-data. Hepatic and intestinal f a-data were used to predict bile excretion potential. Relationships were established, including predicted curves for bile excretion potential and minimum oral bioavailability, and a 4-Class PCS was developed: I (very high P e; elimination mainly by metabolism); II (high P e) and III (intermediate P e and incomplete f a); IV (low P e and f a). The system enables assessment of potential drug–drug transport interactions, and drug and metabolite organ trapping. The PCS and high quality P e-data (with and without active transport) are believed to be useful for predictions and understanding of ADME/PK, elimination routes, and potential interactions and organ trapping/toxicity in humans.

COX-inhibiting nitric oxide donators (CINODs) are a new class of drugs in development for the treatment of acute and chronic pain. They comprise a COX-inhibiting moiety linked to a nitric-oxide-donating component and are designed to provide an innovative mechanism of action of balanced COX inhibition and controlled nitric oxide donation. Through these pathways, CINODs should provide analgesic and anti-inflammatory efficacy, while offering gastrointestinal safety through the tissue-protective effects of nitric oxide donation. AZD3582 [4-(nitrooxy)butyl-(2S)-2-(6-methoxy-2-naphthyl)propanoate] is the first agent in the CINOD class to enter extensive clinical development. Pre-clinical studies demonstrate that AZD3582 has a superior gastrointestinal safety profile to naproxen, while demonstrating analgesic and anti-inflammatory efficacy. In healthy human volunteers, AZD3582 caused little gastrointestinal damage compared with equimolar doses of naproxen. Studies to evaluate the longer-term gastrointestinal safety of AZD3582, alongside its efficacy in alleviating chronic and acute pain, are ongoing.

Cyclooxygenase inhibiting nitric oxide donators (CINODs) are a new class of anti-inflammatory and analgesic drugs that may minimise gastrointestinal toxicity compared with standard non-steroidal anti-inflammatory drugs (NSAIDs) by virtue of nitric oxide donation.A proof of concept study of the gastrointestinal safety of AZD3582, the first CINOD available for human testing, was conducted. Thirty one subjects were randomised to receive placebo, naproxen 500 mg twice daily, or its nitroxybutyl derivative AZD3582 in an equimolar dose (750 mg twice daily) for 12 days in a double blind three period crossover volunteer study. At the start and end of each dosing period, gastroduodenal injury was assessed by endoscopy and small bowel permeability by differential urinary excretion of lactulose and L-rhamnose. Pharmacokinetic profiles were assessed at steady state.On naproxen, the mean total number of gastroduodenal erosions was 11.5 (and one subject developed an acute ulcer) versus 4.1 on AZD3582 (p0.0001). More than half of the subjects had no erosions on AZD3582. Differences were seen for both the stomach and duodenum. Naproxen increased intestinal permeability (lactulose:L-rhamnose ratio 0.030 before v 0.040 after treatment) whereas AZD3582 (0.029 before, 0.029 after; p=0.006 v naproxen) and placebo (0.030 before, 0.028 after; p0.001 v naproxen) did not. The steady state bioavailability of naproxen metabolised from AZD3582 was 95% (95% confidence interval 87-101%) of that after naproxen administration.This human study supports animal data showing reduced gastrointestinal toxicity with the CINOD AZD3582. The potential combination of effective pain relief and gastrointestinal protection offered by AZD3582 warrants further evaluation in human clinical studies.

The purpose of this study was to characterize the rat jejunal passive transport and the possible active efflux of three local anaesthetics, ropivacaine, lidocaine and bupivacaine using two different absorption models, the in situ single-pass intestinal perfusion and the in vitro Ussing chamber model, as well as P-glycoprotein (Pgp)-mediated calcein transport inhibition in Caco-2 cells. Concentration and pH dependence, efflux inhibition by verapamil and digoxin and bi-directional permeability studies were performed to investigate the potential involvement of efflux carriers in the intestinal absorption of the local anaesthetics. In the jejunal perfusion the permeability of these agents appeared to be high, predicting complete intestinal absorption (>90%). There was no effect of the Pgp inhibitors on net absorption for any of the local anaesthetics in the two absorption models. However, in the Ussing chamber at an equal pH of 7.4 at mucosal and serosal sides, the observed jejunal permeability ratios (S-M)/(M-S), of 2.3, 1.8 and 3.0 for ropivacaine, lidocaine and bupivacaine, respectively, indicated at least some involvement of carrier-mediated intestinal secretion. This idea was supported in the calcein AM Pgp transport assay in which two of the tested local anaesthetic agents affected cellular calcein retention. As anticipated for these agents, the mucosal pH conditions were shown to largely affect the gut permeability. The jejunal permeabilities of the local anaesthetics as measured in the two absorption models fitted well in a model comparison that incorporated the permeabilities of six other structurally unrelated drugs. In conclusion, the jejunal permeability of ropivacaine, lidocaine and bupivacaine was high and although evidence was obtained for carrier-mediated intestinal efflux this process appeared not to have a significant influence on the rate and extent of in vivo intestinal absorption. Rather, passive diffusion of these agents seems to be the major mechanism for the intestinal absorption.

The aim of this study was to investigate if the effective in-situ permeability (Peff) of a new growth hormone-releasing peptide, hexarelin, along rat intestine was enhanced by a lipid matrix drug-delivery system comprising a mixture of soybean phosphatidyl choline and medium-chain monoacylglycerol (PC-MG). The study was performed with and without a protease inhibitor, Pefabloc SC. To enable better understanding of the mechanism of action of this delivery system we also studied the uptake of a small hydrophilic molecule, atenolol. PC-MG at a concentration of 15 mmol L−1 increased the jejunal Peff of hexarelin approximately 20-fold, both in the presence and absence of Pefabloc SC, whereas Peff was not increased in the ileum and colon. PC-MG had no effect on the jejunal, ileal and colonic Peff of atenolol. Complete recovery of the non-absorbable molecule PEG 4000 showed that functional intestinal viability was maintained in all experiments. Although the results obtained in this study are promising, pharmacokinetic and toxicological studies are required to investigate if this delivery system is a suitable and safe candidate for improving the oral bioavailability of hexarelin.

Because the absorption of orally administered drugs depends on intestinal permeability, we have investigated how absorptive capacity varies from the proximal to distal intestine in rats. The effective permeabilities of compounds with a range of physicochemical properties and different absorption mechanisms were estimated by use of a previously validated in-situ, single-pass perfusion model. The low colonic permeabilities of d-glucose and l-dopa indicate the absence or low capacity of the glucose-and amino-acid-transporters in this region. With the exception of the small and moderately lipophilic nonsteroidal anti-inflammatory drug, naproxen, for which permeability was maintained throughout the intestine, the passive intestinal permeabilities for hydrophilic and lipophilic drugs were approximately twice as high in the jejunum and ileum as in the colon. These observations are in accord with those made in recent studies. However, the reasons for the high colonic permeability of non-steroidal anti-inflammatory drugs, and results obtained in previous animal experiments demonstrating that the colon is the region of the intestine with the highest absorptive capacity were not fully clarified. These data show that the permeability to hydrophilic and lipophilic drugs decreases along the intestine, whereas it is maintained throughout the intestine for the small and moderately lipophilic naproxen. Further investigations are required to clarify the interplay between membrane composition, fluidity and permeability under various conditions in different absorption models.