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- Fagerholm, Urban, et al.
(författare)
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Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology
- 2021
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Ingår i: Molecules. - : MDPI. - 1431-5157 .- 1420-3049. ; 26:9
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Tidskriftsartikel (refereegranskat)abstract
- Oral bioavailability (F) is an essential determinant for the systemic exposure and dosing regimens of drug candidates. F is determined by numerous processes, and computational predictions of human estimates have so far shown limited results. We describe a new methodology where F in humans is predicted directly from chemical structure using an integrated strategy combining 9 machine learning models, 3 sets of structural alerts, and 2 physiologically-based pharmacokinetic models. We evaluate the model on a benchmark dataset consisting of 184 compounds, obtaining a predictive accuracy (Q2) of 0.50, which is successful according to a pharmaceutical industry proposal. Twenty-seven compounds were found (beforehand) to be outside the main applicability domain for the model. We compare our results with interspecies correlations (rat, mouse and dog vs. human) using the same dataset, where animal vs. human-correlations (R2) were found to be 0.21 to 0.40 and maximum prediction errors were smaller than maximum interspecies differences. We conclude that our method has sufficient predictive accuracy to be practically useful with applications in human exposure and dose predictions, compound optimization and decision making, with potential to rationalize drug discovery and development and decrease failures and overexposures in early clinical trials with candidate drugs.
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- Fagerholm, Urban, et al.
(författare)
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Comparison between lab variability and in silico prediction errors for the unbound fraction of drugs in human plasma
- 2021
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Ingår i: Xenobiotica. - : Taylor & Francis. - 0049-8254 .- 1366-5928. ; 51:10, s. 1095-1100
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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- Fagerholm, Urban, et al.
(författare)
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In Silico Prediction of Human Clinical Pharmacokinetics with ANDROMEDA by Prosilico : Predictions for an Established Benchmarking Data Set, a Modern Small Drug Data Set, and a Comparison with Laboratory Methods
- 2023
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Ingår i: ATLA (Alternatives to Laboratory Animals). - : SAGE Publications. - 0261-1929. ; 51:1, s. 39-54
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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- Fagerholm, Urban, et al.
(författare)
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In silico prediction of volume of distribution of drugs in man using conformal prediction performs on par with animal data-based models
- 2021
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Ingår i: Xenobiotica. - : Taylor & Francis. - 0049-8254 .- 1366-5928. ; 51:12, s. 1366-1371
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Tidskriftsartikel (refereegranskat)abstract
- 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 <2-fold prediction error for our method were 2.4-fold and 64%, respectively. 69% of test compounds had an observed Vss within the prediction interval at a 70% confidence level. In comparison, 2.2-, 2.9- and 3.1-fold mean errors and 69, 64 and 61% of predictions with <2-fold error was reached with rat-to-man and allometric scaling and Rodgers-Lukova method, respectively.We conclude that our method has theoretically proven validity that was empirically confirmed, and showing predictive accuracy on par with animal models and superior to an alternative widely used in silico-based method. The option for the user to select the level of confidence in predictions offers better guidance on how to optimise Vss in drug discovery applications.
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| 8. |
- Fagerholm, Urban, et al.
(författare)
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In Silico Predictions of the Gastrointestinal Uptake of Macrocycles in Man Using Conformal Prediction Methodology
- 2022
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Ingår i: Journal of Pharmaceutical Sciences. - : Elsevier. - 0022-3549 .- 1520-6017. ; 111:9, s. 2614-2619
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Tidskriftsartikel (refereegranskat)abstract
- 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(a)), maximum fraction dissolved (f(diss)), substrate specificities for major efflux transporters and total fraction absorbed (f(a,tot)) for a selected set of designed macrocyclic compounds (n = 37; MW 407-889 g/mol) and macrocyclic drugs (n = 16; MW 734-1203 g/mole) in vivo in man. Major aims were to increase the understanding of oral absorption of macrocycles and further validate our methodology. We predicted designed macrocycles to have high f(a )and low to high f(diss) and f(a,tot, )and average estimates were higher than for the larger macrocyclic drugs. With few exceptions, compounds were predicted to be effluxed and well absorbed. A 2-fold median prediction error for f(a,tot )was achieved for macrocycles (validation set). Advantages with our methodology include that it enables predictions for macrocycles with low permeability, Caco-2 recovery and solubility (BCS IV), and provides prediction intervals and guides optimization of absorption. The understanding of oral absorption of macrocycles was increased and the methodology was validated for prediction of the uptake of macrocycles in man.(C) 2022 American Pharmacists Association. Published by Elsevier Inc. All rights reserved.
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| 9. |
- Fagerholm, Urban, et al.
(författare)
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In silico predictions of the human pharmacokinetics/toxicokinetics of 65 chemicals from various classes using conformal prediction methodology
- 2022
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Ingår i: Xenobiotica. - : Taylor & Francis Group. - 0049-8254 .- 1366-5928. ; 52:2, s. 113-118
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Tidskriftsartikel (refereegranskat)abstract
- 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 <2- and <5-fold, respectively. Predicted and observed PK/TK for the chemicals was generally within the range for pharmaceutical drugs. The results validated the new integrated system for prediction of the human PK/TK for different chemicals and added important missing information. No general difference in PK/TK-characteristics was found between the selected chemicals and pharmaceutical drugs.
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| 10. |
- Fagerholm, Urban, et al.
(författare)
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The Impact of Reference Data Selection for the Prediction Accuracy of Intrinsic Hepatic Metabolic Clearance
- 2022
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Ingår i: Journal of Pharmaceutical Sciences. - : Elsevier. - 0022-3549 .- 1520-6017. ; 111:9, s. 2645-2649
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Tidskriftsartikel (refereegranskat)abstract
- In vitro-in vivo prediction results for hepatic metabolic clearance (CLH) and intrinsic CLH (CLint) vary widely among studies. Reasons are not fully investigated and understood. The possibility to select favorable reference data for in vivo CLH and CLint and unbound fraction in plasma (f(u)) is among possible explanations. The main objective was to investigate how reference data selection influences log in vitro and in vivo CLint-correlations (r(2)). Another aim was to make a head-to-head comparison vs an in silico prediction method. Human hepatocyte CLint-data for 15 compounds from two studies were selected. These were correlated to in vivo CLint estimated using different reported CLH- and f(u)-estimates. Depending on the choice of reference data, r(2) from two studies were 0.07 to 0.86 and 0.06 to 0.79. When using average reference estimates a r(2) of 0.62 was achieved. Inclusion of two outliers in one of the studies resulted in a r(2) of 0.38, which was lower than the predictive accuracy (q(2)) for the in silico method (0.48). In conclusion, the selection of reference data appears to play a major role for demonstrated predictions and the in silico method showed higher accuracy and wider range than hepatocytes for human in vivo CLint-predictions. (C) 2022 Published by Elsevier Inc. on behalf of American Pharmacists Association.
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| 11. |
- Lundquist, Patrik, et al.
(författare)
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Prediction of In Vivo Rat Biliary Drug Clearance from an In Vitro Hepatocyte Efflux Model
- 2014
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Ingår i: Drug Metabolism And Disposition. - : American Society for Pharmacology & Experimental Therapeutics (ASPET). - 0090-9556 .- 1521-009X. ; 42:3, s. 459-468
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 12. |
- Sohlenius-Sternbeck, Anna-Karin, et al.
(författare)
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The volume of distribution is an indicator of poor in vitro-in vivo extrapolation of clearance for acidic drugs in the rat
- 2013
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Ingår i: Xenobiotica. - : Informa UK Limited. - 0049-8254 .- 1366-5928. ; 43:8, s. 671-678
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Tidskriftsartikel (refereegranskat)abstract
- 1. We applied the regression offset approach to predict rat in vivo intrinsic clearance (CL(int)) 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 (V(d)), molecular weight (Mw) and CL.2. A correlation between poor in vitro-in vivo extrapolation (IVIVE) and V(d) values distinct from the V(d) of albumin (0.1-0.2 L/kg) was revealed. For compounds with a V(d) value above 0.5 L/kg, 0% of the predictions of in vivo CL(int) was within twofold of the observed value, compared to 69% for compounds with a V(d) value below 0.5 L/kg.3. Compounds with a Mw below 450 g/mol demonstrated more accurate in vivo CL(int) 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 V(d) (i.e. around the V(d) 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.
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