Biology subjects

Fagerholm, U.

Publications and source records attributed to Fagerholm, U..

18 recordsLinked to original sources

bioRxiv2024

Evaluation of common in vitro assays for the prediction of oral bioavailability and hepatic metabolic clearance in humans

IntroductionIntrinsic hepatic metabolic clearance (CLint) measured with human hepatocytes, apparent intestinal permeability (Papp) obtained using the Caco-2 model, unbound fraction in plasma (fu) and blood-to-plasma concentration ratio (Cbl/Cpl) are commonly used for predicting the hepatic clearance (CLH) and oral bioavailability (F) of drug candidates in humans. The primary objective was to select drugs whose in vitro hepatocyte CLint, Caco-2 Papp, fu and Cbl/Cpl have been measured in various laboratories and studies, and estimate correlation coefficients (R2) for predicted and observed F and log plasma CLH. Secondary aims were to estimate the laboratory/study variability and its impact on predictions and to compare results to in silico and animal model-based predictions. Materials and MethodsA literature search was done in order to find unbound hepatocyte CLint, (and corresponding predicted in vivo CLint), Caco-2 Papp, fu and Cbl/Cpl data. Compounds with multiple measurements for the four assays, without significant in vivo solubility/dissolution limitations and with known in vivo CLH and F, were selected. Min, max and mean estimates were used in the analysis. Results and DiscussionThirty-two compounds with data (in total 561 estimates) produced by 21 major pharmaceutical companies and universities met the inclusion criteria. The predicted vs observed R2 for log mean CLint, log mean CLH and mean F were 0.32, 0.08 and 0.20, respectively. Exclusion of atenolol increased the R2 for CLH to 0.20. R2-values were considerably lower than those presented in many studies, which seems to be explained by selection bias (choosing favorable reference values). There was considerable interstudy variability for measured and predicted CLint (80- and 1,476-fold mean and max differences, respectively) and measured fu (6.6- and 50-fold mean and max differences, respectively). For F, higher predictive performance was found for in silico (Q2=0.58; head-to-head) and animal in vivo models (R2=0.30). ConclusionThe combination of data from many laboratories and the use of mean values resulted in reduced selection bias and predictive accuracy. Overall, the predictive accuracy (here R2) for log CLint, log CLH and F was low to moderately low (0.08-0.32). The halved R2 compared to individual studies where high performance was demonstrated seems to be explained be selection bias (enabled by large data variability). Animal in vivo models, and in particular, in silico methodology, outperformed in vitro methodology for the prediction of F in man.

pharmacology and toxicology↗

bioRxiv2023

ANDROMEDA by Prosilico and log D outperform human hepatocytes for the prediction of intrinsic hepatic metabolic clearance of carboxylic acids

IntroductionExtrahepatic metabolism/conjugation, deconjugation of their metabolites, and low and varying unbound fraction in plasma (fu), is characteristic for carboxylic drugs. Thus, it is comparably difficult to estimate their in vivo intrinsic hepatic metabolic clearance (CLint) and hepatic CL (CLH) and to predict their in vivo CLint, CLH and CL. One objective was to investigate the laboratory variability of fu and CLint for carboxylic acids. Another objective was to compare human hepatocytes, measured log D and the software ANDROMEDA with regards to prediction of human in vivo CLint of carboxylic acids. Materials and MethodsMeasured unbound hepatocyte CLint, non-renal CL (surrogate for CLH), non-renal CLint (surrogate for hepatic metabolic CLint), log D and fu data were taken from studies in the literature. ANDROMEDA (by Prosilico; version 1.0) prediction software was used for in silico predictions of CLint for carboxylic acids not used in the training set of its CLint-model. Results and DiscussionMean and maximum differences between highest and lowest reported in vivo CLint predicted from hepatocyte CLint were 210- and 1,476-fold (n=8), respectively. Corresponding estimates for in vitro fu were 19- and 50-fold, respectively. The data set with the apparently highest number of carboxylic acids contains 39 carboxylic acids with in vitro CLint and log D (both measured at the same laboratory), in vivo CLint and in vitro fu. 18 carboxylic acids were excluded as their in vitro CLint was below the limit of quantification. The correlation coefficient (R2) for log hepatocyte predicted in vivo CLint vs log in vivo CLint was 0.34. The corresponding R2 for log D vs log in vivo CLint was 0.40 (0.47 for 64 carboxylic acids). The Q2 (forward-looking R2) for in silico (ANDROMEDA) predicted and measured log in vivo CLint for 12 carboxylic acids was 0.86. The corresponding R2 for hepatocytes and log D were 0.67 and 0.66, respectively. ANDROMEDA produced a lower maximum prediction error compared to hepatocytes and also predicted the in vivo CLint for all carboxylic acids out of reach for the hepatocyte assay. ConclusionVery large interlaboratory variability was demonstrated for plasma protein binding and hepatocyte assays. Log D, and especially ANDROMEDA, outperformed the hepatocyte assay for the prediction of CLint of carboxylic acids in vivo in man.

pharmacology and toxicology↗

bioRxiv2023

Exploring Relationships Between In Vitro Aqueous Solubility and Permeability and In Vivo Fraction Absorbed

IntroductionSolubility/dissolution and permeability are essential determinants of gastrointestinal absorption of drugs. In vitro aqueous solubility (S) and apparent permeability (Papp) are commonly used as measurements and predictors of in vivo fraction absorbed (fa) and BCS-classing in humans. The objective of this study was to explore the relationships between in vitro aqueous S and Dose number (Do) and in vivo fa and in vitro Papp and in vivo fa and the predictive power of in vitro aqueous S, Do and Papp. MethodsIn vitro and in vivo data were taken from studies in the literature and correlated. In vitro S data were produced in various laboratories and with different methodologies. In vitro Papp data were produced using Caco-2 and MDCK cells in various laboratories and Caco-2 and RRCK cells in one laboratory each. Do was estimated as oral dose / (S * 250 mL). Results452 S data and 1480 Papp data were found and used. There was no correlation (R2=0.0) between in vitro log S and Do vs in vivo fa, not even at S<1 mg/L or not for compounds with <90 % and <30 % in vivo fa. A R2 of 0.43 was found between log Caco-2 Papp and in vivo fa. The corresponding R2 for Caco-2 from one laboratory was 0.65. The interlaboratory R2 for the Caco-2 model was 0.48. R2-estimates for Caco-2 vs MDCK and Caco-2 vs RRCK Papp were 0.23 and 0.21, respectively. Discussion and ConclusionAqueous S appears to have no predictive value of in vivo fa in humans, not even at low S or after correction for dose. The shows that one should not base human biopharmaceutical predictions based on aqueous S. Log Caco-2 Papp explains about half of the variance of in vivo fa in humans. The poor correlations found between Caco-2 and the two other Papp-models (MDCK and RRCK) demonstrate considerable methodological differences. The unexplained variance does not appear to be explained by S and dose, but rather by in vitro-in vivo difference in permeability and poor/absent relationship between in vitro S and in vivo dissolution potential.

pharmacology and toxicology↗

bioRxiv2023

In silico predictions of the hepatic metabolic clearance in humans for 10 drugs with highly variable in vitro pharmacokinetics

AO_SCPLOWBSTRACTC_SCPLOWChallenges/problems for in vitro methodologies 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 predicted in vivo hepatic metabolic clearance (CLH) for the drugs were 529- and 2436-fold, respectively. The in vivo CLH was predicted using in vitro CLint and fu data from the various highly sources and using our in silico methodology. The main aim was to compare the predictive accuracies for the in vitro and in silico methodologies. Prediction errors for in vitro methodology ranged from 1.1-to 578-fold, with an average of 150-fold for lowest predicted estimates and 16-fold for highest predicted estimates. The in vitro based predictions produced 36-to 38-fold higher average and maximum prediction errors than the in silico methodology, respectively. Mean and maximum in silico prediction errors were 4.2- and 15-fold, respectively, which is consistent with earlier results. In contrast to the in vitro methodology the in silico models did not predict high hepatic extraction ratio for drugs with low CLH. Overall, the in silico method clearly outperformed in vitro data for prediction of CLH in man for 10 drugs with large interlaboratory variability.

pharmacology and toxicology↗

bioRxiv2023

Prediction of the Human Pharmacokinetics of 30 Modern Antibiotics Using the ANDROMEDA Software

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 <3-fold error. 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 antibiotic drugs, which to a great extent demonstrate pharmacokinetic characteristics challenging for laboratory methods (metabolic stability, limited permeability, efflux and multiple elimination pathways). Advantages with ANDROMEDA include that results are produced without the use of animals and cells and that predictions and decision-making can be done already at the design stage.

pharmacology and toxicology↗

bioRxiv2023

Application of the ANDROMEDA Software for Prediction of the Human Pharmacokinetics of Modern Anticancer Drugs

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.

pharmacology and toxicology↗

bioRxiv2022

Comparing in silico and in vitro methods for classification of BCS II and CYP3A4 and MDR-1 substrate specificity

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 corresponding in silico methods. 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 using in silico methodology 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 an in vivo fraction absorbed (fa) of [≥]90 % and belong to in vivo BCS I. With in silico methodology, 74 % correct BCS-classing was reached for the same set of compounds. The mean prediction error for fa was 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. With in silico methodology, 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 and in vivo fa) and MDR-1 (34 % inconsistency between sources) and CYP3A4 (10 % inconsistency between sources) substrate specificities. Corresponding estimates obtained with in silico methodology are 22, 13 and 15 %, respectively, demonstrating the power and applicability of such technology.

pharmacology and toxicology↗

bioRxiv2022

Predicting the Influence of Fat Food Intake on the Absorption and Systemic Exposure of Modern Small Drugs using ANDROMEDA by Prosilico Software

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSIntroductionC_ST_ABSThe ANDROMEDA software by Prosilico has previously been successfully applied and validated for predictions of absorption characteristics of small drugs in man. The influence 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 (including those marketed in 2021) and compare results with corresponding measured clinical estimates. Actual dose sizes were considered. Another objective was to compare the performance of ANDROMEDA vs physiologically based pharmacokinetic (PBPK) modelling and simulations by The Food Effect PBPK IQ Working Group. PBPK results generated using Simcyp and GastroPlus software were based on various physicochemical, in vitro and in vivo data and a decision tree for model verification and optimization. Results and Discussion63 drugs, including 17 new drugs, with observed AUCfed/AUCfast between 0.2 and 5.5 were found and used for this evaluation. Predicted AUCfed/AUCfast had mean and maximum errors of 1.5- and 4.1-fold, respectively, and the predictive accuracy (correlation between predicted and observed AUCfed/AUCfast; Q2) was 0.3. 14 % of predictions had >2-fold error. For 72 % of drugs, food interaction class was correctly predicted. The level of predictive accuracy was overall similar to results obtained with PBPK modelling and simulations, however, with lower maximum error and higher compound coverage. With PBPK models, maximum simulation error was 7.7-fold and 3 highly lipophilic compounds were not possible to simulate. ConclusionThe results validate ANDROMEDA for prediction of fat food-drug interaction size for small drugs in man. Major advantages with the methodology include that prediction results are produced directly from molecular structure and oral dose and are similar to PBPK-simulation results obtained using in vitro and clinical data. Furthermore, ANDROMEDA showed lower maximum errors and wider compound range.

pharmacology and toxicology↗

bioRxiv2022

Predicting Gastrointestinal Absorption of Prodrugs and their Drugs with the ANDROMEDA by Prosilico Software

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSIntroductionC_ST_ABSSome 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,passive and fdiss for 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 fa were 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 fa for non-hydrolyzed ester prodrugs and drugs combined (predictive accuracy) was 0.6. ConclusionProdrugs and drugs had similar average predicted fa,passive and fdiss, and most had or were predicted to have at least 50 % fa. The fa for 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.

pharmacology and toxicology↗

bioRxiv2022

Evaluation of the reliability and applicability of human unbound brain-to-plasma concentration ratios

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 estimated in vivo in rats and monkeys and predicted using in silico methodology. 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,brain in rats, monkeys and humans, measured and in silico predicted MDR-1 and BCRP substrate specificities and in silico predicted passive Pe were used for the analysis. In silico predictions 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,brain was expected. However, no correlation between rat and human Kp,uu,brain was found (R^2=0.01; n=13). There was no (as also anticipated) correlation between passive Pe and human Kp,uu,brain (R^2=0.04; n=16) and compounds with measured or predicted efflux did not have lower Kp,uu,brain than compounds without efflux. The compound with highest Kp,uu,brain in man (2.8) is effluxed and predicted to have high passive Pe and has no apparent efflux at the rat BBB. The MDR-1 substrate with highest Kp,uu,brain in rat (2.4) has very low Kp,uu,brain in 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.

pharmacology and toxicology↗

bioRxiv2022

Validation of predicted conformal intervals for prediction of human clinical pharmacokinetics

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 (predicted vs observed 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,passive and fdiss for 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,passive and fdiss, respectively. 78 % (expected 70 %) of observed data were within 70 % confidence intervals for the parameters. 70 % of predictions of Vss, fu, CLint fa,passive and fdiss are 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.

pharmacology and toxicology↗

bioRxiv2022

ANDROMEDA by Prosilico Software Successfully Predicts Human Clinical Pharmacokinetics of 70 Drugs Out of Reach for In Vitro Methods

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSIntroductionC_ST_ABSIn vitro measurements 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 for in vitro PK-methods and (if possible) predict corresponding human clinical estimates using the ANDROMEDA by Prosilico software. In vitro methods selected for the investigation were human microsomes and hepatocytes for measuring and predicting intrinsic hepatic metabolic clearance (CLint), Caco-2 and Ralph Russ canine kidney cells (RRCK) 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 of in vivo dissolution potential (fdiss). Results and ConclusionAs many as 329 non-quantifiable in vitro PK-measurements for 300 compounds were found in the literature: 191 for CLint, 101 for Papp, 11 for fu and 26 for S. ANDROMEDA was successful in predicting all corresponding clinical PK-estimates for the selection of compounds with non-quantifiable in vitro PK, and predicted estimates (1.6-fold median prediction error; n=159) were generally in line with observed in vivo data and results/problems at in vitro laboratories. Thus, ANDROMEDA is applicable for predicting human clinical PK for compounds out of reach for laboratory methods.

pharmacology and toxicology↗

bioRxiv2022

Prediction and Classification of the Uptake and Disposition of Antidepressants and New CNS-Active Drugs in the Human Brain using the ANDROMEDA by Prosilico Software and Brainavailability-Matrix

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSPassive 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 Pe x 4 fb,brain classes, 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.

pharmacology and toxicology↗

bioRxiv2022

An analysis of laboratory variability and thresholds for human in vitro ADME/PK methods

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSIntroductionC_ST_ABSVarious 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.

pharmacology and toxicology↗

bioRxiv2022

Investigation of Molecular Weights and Pharmacokinetic Characteristics of Older and Modern Small Drugs

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSA 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 MW<1500 g/mole) marketed before 2010 (n=277) and MW and in silico predicted (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 to in silico predictions) 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 fu increases 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.

pharmacology and toxicology↗

bioRxiv2022

In vitro to in vivo pharmacokinetic translation guidance

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, Papp vs 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 {micro}L/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.

pharmacology and toxicology↗

bioRxiv2022

Prediction of Biopharmaceutical Characteristics of PROTACs using the ANDROMEDA by Prosilico Software

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 clinical in vivo dissolution potential (fdiss) and fraction absorbed (fa) of 23 PROTACs at a dose level of 50 mg and to explore whether there is any relationship between in vitro S and in silico predicted in vivo fdiss. ResultsIn silico predictions 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 between in vitro S and predicted in vivo fdiss was demonstrated (R2=0.26). Predicted human fa (27 %) and oral bioavailability (20 %) of ARV-110 (a PROTAC with some available in vivo PK data in rodents and man) were consistent with data obtained in rodents (estimated fa approximately 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 fdiss and fa for all the chosen PROTACs and showed limited fdiss, Pe and fa and dose-dependent fdiss and fa. One available example shows promise for the applicability of ANDROMEDA for predicting biopharmaceutics of PROTACs in vivo in man. Weak to modest correlations between S and fdiss and a considerable portion of compounds with non-quantifiable S limit the use of S-data to predict the uptake of PROTACs.

pharmacology and toxicology↗

bioRxiv2022

Using the ANDROMEDA by Prosilico Software for Prediction of the Human Pharmacokinetics of 4 Compounds of Natural Origin - Colistin, Curucumin, UCN-01 and Voclosporin

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSIt 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.

pharmacology and toxicology↗