The Experts below are selected from a list of 69 Experts worldwide ranked by ideXlab platform
Maria L. H. Vlaming - One of the best experts on this subject based on the ideXlab platform.
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Generation of Bayesian prediction models for OATP-mediated drug–drug interactions based on inhibition screen of OATP1B1, OATP1B1∗15 and OATP1B3
European Journal of Pharmaceutical Sciences, 2015Co-Authors: Evita Van De Steeg, Jennifer Venhorst, Harm T. Jansen, I.h.g. Nooijen, Heleen M. Wortelboer, Jack Degroot, Maria L. H. VlamingAbstract:Human organic anion-transporting polypeptide 1B1 (OATP1B1) and OATP1B3 are important hepatic uptake transporters. Early assessment of OATP1B1/1B3-mediated drug-drug interactions (DDIs) is therefore important for successful drug development. A promising approach for early screening and prediction of DDIs is computational modeling. In this study we aimed to generate a rapid, single Bayesian prediction model for OATP1B1, OATP1B1∗15 and OATP1B3 inhibition. Besides our previously generated HEK-OATP1B1 and HEK-OATP1B1∗15 cells, we now generated and characterized HEK-OATP1B3 cells. Using these cell lines we investigated the inhibitory potential of 640 FDA-approved drugs from a commercial library (10 μM) on the uptake of [3H]-estradiol-17β-d-glucuronide (1 μM) by OATP1B1, OATP1B1∗15, and OATP1B3. Using a cut-off of ≥60% inhibition, 8% and 7% of the 640 drugs were potent OATP1B1 and OATP1B1∗15 inhibitors, respectively. Only 1% of the tested drugs significantly inhibited OATP1B3, which was not sufficient for Bayesian modeling. Modeling of OATP1B1 and OATP1B1∗15 inhibition revealed that presence of conjugated systems and (hetero)cycles with acceptor/donor atoms in- or outside the ring enhance the probability of a molecule binding these transporters. The overall performance of the model for OATP1B1 and OATP1B1∗15 was ≥80%, including evaluation with a true external test set. Our Bayesian classification model thus represents a fast, inexpensive and robust means of assessing potential binding of new chemical entities to OATP1B1 and OATP1B1∗15. As such, this model may be used to rank compounds early in the drug development process, helping to avoid adverse effects in a later stage due to inhibition of OATP1B1 and/or OATP1B1∗15. Chemicals/CAS: abamectin, 71751-41-2; acemetacin, 53164-05-9; atazanavir, 198904-31-3; Bromocriptine Mesilate, 22260-51-1; clarithromycin, 81103-11-9; clobetasol propionate, 25122-46-7; dihydroergocristine methanesulfonate, 24730-10-7; dipyridamole, 58-32-2; docetaxel, 114977-28-5; estradiol, 50-28-2; fluindostatin, 93957-54-1; fosinopril, 88889-14-9, 98048-97-6; losartan, 114798-26-4; mifepristone, 84371-65-3; nicardipine, 54527-84-3, 55985-32-5; olmesartan, 144689-63-4; pranlukast, 103177-37-3; pyrantel embonate, 22204-24-6; rapamycin, 53123-88-9; rifampicin, 13292-46-1; rifamycin, 6998-60-3, 14897-39-3, 15105-92-7; salazosulfapyridine, 599-79-1; suramin, 129-46-4, 145-63-1; telmisartan, 144701-48-4; tibolone, 5630-53-5; troglitazone, 97322-87-7
Evita Van De Steeg - One of the best experts on this subject based on the ideXlab platform.
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Generation of Bayesian prediction models for OATP-mediated drug–drug interactions based on inhibition screen of OATP1B1, OATP1B1∗15 and OATP1B3
European Journal of Pharmaceutical Sciences, 2015Co-Authors: Evita Van De Steeg, Jennifer Venhorst, Harm T. Jansen, I.h.g. Nooijen, Heleen M. Wortelboer, Jack Degroot, Maria L. H. VlamingAbstract:Human organic anion-transporting polypeptide 1B1 (OATP1B1) and OATP1B3 are important hepatic uptake transporters. Early assessment of OATP1B1/1B3-mediated drug-drug interactions (DDIs) is therefore important for successful drug development. A promising approach for early screening and prediction of DDIs is computational modeling. In this study we aimed to generate a rapid, single Bayesian prediction model for OATP1B1, OATP1B1∗15 and OATP1B3 inhibition. Besides our previously generated HEK-OATP1B1 and HEK-OATP1B1∗15 cells, we now generated and characterized HEK-OATP1B3 cells. Using these cell lines we investigated the inhibitory potential of 640 FDA-approved drugs from a commercial library (10 μM) on the uptake of [3H]-estradiol-17β-d-glucuronide (1 μM) by OATP1B1, OATP1B1∗15, and OATP1B3. Using a cut-off of ≥60% inhibition, 8% and 7% of the 640 drugs were potent OATP1B1 and OATP1B1∗15 inhibitors, respectively. Only 1% of the tested drugs significantly inhibited OATP1B3, which was not sufficient for Bayesian modeling. Modeling of OATP1B1 and OATP1B1∗15 inhibition revealed that presence of conjugated systems and (hetero)cycles with acceptor/donor atoms in- or outside the ring enhance the probability of a molecule binding these transporters. The overall performance of the model for OATP1B1 and OATP1B1∗15 was ≥80%, including evaluation with a true external test set. Our Bayesian classification model thus represents a fast, inexpensive and robust means of assessing potential binding of new chemical entities to OATP1B1 and OATP1B1∗15. As such, this model may be used to rank compounds early in the drug development process, helping to avoid adverse effects in a later stage due to inhibition of OATP1B1 and/or OATP1B1∗15. Chemicals/CAS: abamectin, 71751-41-2; acemetacin, 53164-05-9; atazanavir, 198904-31-3; Bromocriptine Mesilate, 22260-51-1; clarithromycin, 81103-11-9; clobetasol propionate, 25122-46-7; dihydroergocristine methanesulfonate, 24730-10-7; dipyridamole, 58-32-2; docetaxel, 114977-28-5; estradiol, 50-28-2; fluindostatin, 93957-54-1; fosinopril, 88889-14-9, 98048-97-6; losartan, 114798-26-4; mifepristone, 84371-65-3; nicardipine, 54527-84-3, 55985-32-5; olmesartan, 144689-63-4; pranlukast, 103177-37-3; pyrantel embonate, 22204-24-6; rapamycin, 53123-88-9; rifampicin, 13292-46-1; rifamycin, 6998-60-3, 14897-39-3, 15105-92-7; salazosulfapyridine, 599-79-1; suramin, 129-46-4, 145-63-1; telmisartan, 144701-48-4; tibolone, 5630-53-5; troglitazone, 97322-87-7
Jack Degroot - One of the best experts on this subject based on the ideXlab platform.
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Generation of Bayesian prediction models for OATP-mediated drug–drug interactions based on inhibition screen of OATP1B1, OATP1B1∗15 and OATP1B3
European Journal of Pharmaceutical Sciences, 2015Co-Authors: Evita Van De Steeg, Jennifer Venhorst, Harm T. Jansen, I.h.g. Nooijen, Heleen M. Wortelboer, Jack Degroot, Maria L. H. VlamingAbstract:Human organic anion-transporting polypeptide 1B1 (OATP1B1) and OATP1B3 are important hepatic uptake transporters. Early assessment of OATP1B1/1B3-mediated drug-drug interactions (DDIs) is therefore important for successful drug development. A promising approach for early screening and prediction of DDIs is computational modeling. In this study we aimed to generate a rapid, single Bayesian prediction model for OATP1B1, OATP1B1∗15 and OATP1B3 inhibition. Besides our previously generated HEK-OATP1B1 and HEK-OATP1B1∗15 cells, we now generated and characterized HEK-OATP1B3 cells. Using these cell lines we investigated the inhibitory potential of 640 FDA-approved drugs from a commercial library (10 μM) on the uptake of [3H]-estradiol-17β-d-glucuronide (1 μM) by OATP1B1, OATP1B1∗15, and OATP1B3. Using a cut-off of ≥60% inhibition, 8% and 7% of the 640 drugs were potent OATP1B1 and OATP1B1∗15 inhibitors, respectively. Only 1% of the tested drugs significantly inhibited OATP1B3, which was not sufficient for Bayesian modeling. Modeling of OATP1B1 and OATP1B1∗15 inhibition revealed that presence of conjugated systems and (hetero)cycles with acceptor/donor atoms in- or outside the ring enhance the probability of a molecule binding these transporters. The overall performance of the model for OATP1B1 and OATP1B1∗15 was ≥80%, including evaluation with a true external test set. Our Bayesian classification model thus represents a fast, inexpensive and robust means of assessing potential binding of new chemical entities to OATP1B1 and OATP1B1∗15. As such, this model may be used to rank compounds early in the drug development process, helping to avoid adverse effects in a later stage due to inhibition of OATP1B1 and/or OATP1B1∗15. Chemicals/CAS: abamectin, 71751-41-2; acemetacin, 53164-05-9; atazanavir, 198904-31-3; Bromocriptine Mesilate, 22260-51-1; clarithromycin, 81103-11-9; clobetasol propionate, 25122-46-7; dihydroergocristine methanesulfonate, 24730-10-7; dipyridamole, 58-32-2; docetaxel, 114977-28-5; estradiol, 50-28-2; fluindostatin, 93957-54-1; fosinopril, 88889-14-9, 98048-97-6; losartan, 114798-26-4; mifepristone, 84371-65-3; nicardipine, 54527-84-3, 55985-32-5; olmesartan, 144689-63-4; pranlukast, 103177-37-3; pyrantel embonate, 22204-24-6; rapamycin, 53123-88-9; rifampicin, 13292-46-1; rifamycin, 6998-60-3, 14897-39-3, 15105-92-7; salazosulfapyridine, 599-79-1; suramin, 129-46-4, 145-63-1; telmisartan, 144701-48-4; tibolone, 5630-53-5; troglitazone, 97322-87-7
Jennifer Venhorst - One of the best experts on this subject based on the ideXlab platform.
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Generation of Bayesian prediction models for OATP-mediated drug–drug interactions based on inhibition screen of OATP1B1, OATP1B1∗15 and OATP1B3
European Journal of Pharmaceutical Sciences, 2015Co-Authors: Evita Van De Steeg, Jennifer Venhorst, Harm T. Jansen, I.h.g. Nooijen, Heleen M. Wortelboer, Jack Degroot, Maria L. H. VlamingAbstract:Human organic anion-transporting polypeptide 1B1 (OATP1B1) and OATP1B3 are important hepatic uptake transporters. Early assessment of OATP1B1/1B3-mediated drug-drug interactions (DDIs) is therefore important for successful drug development. A promising approach for early screening and prediction of DDIs is computational modeling. In this study we aimed to generate a rapid, single Bayesian prediction model for OATP1B1, OATP1B1∗15 and OATP1B3 inhibition. Besides our previously generated HEK-OATP1B1 and HEK-OATP1B1∗15 cells, we now generated and characterized HEK-OATP1B3 cells. Using these cell lines we investigated the inhibitory potential of 640 FDA-approved drugs from a commercial library (10 μM) on the uptake of [3H]-estradiol-17β-d-glucuronide (1 μM) by OATP1B1, OATP1B1∗15, and OATP1B3. Using a cut-off of ≥60% inhibition, 8% and 7% of the 640 drugs were potent OATP1B1 and OATP1B1∗15 inhibitors, respectively. Only 1% of the tested drugs significantly inhibited OATP1B3, which was not sufficient for Bayesian modeling. Modeling of OATP1B1 and OATP1B1∗15 inhibition revealed that presence of conjugated systems and (hetero)cycles with acceptor/donor atoms in- or outside the ring enhance the probability of a molecule binding these transporters. The overall performance of the model for OATP1B1 and OATP1B1∗15 was ≥80%, including evaluation with a true external test set. Our Bayesian classification model thus represents a fast, inexpensive and robust means of assessing potential binding of new chemical entities to OATP1B1 and OATP1B1∗15. As such, this model may be used to rank compounds early in the drug development process, helping to avoid adverse effects in a later stage due to inhibition of OATP1B1 and/or OATP1B1∗15. Chemicals/CAS: abamectin, 71751-41-2; acemetacin, 53164-05-9; atazanavir, 198904-31-3; Bromocriptine Mesilate, 22260-51-1; clarithromycin, 81103-11-9; clobetasol propionate, 25122-46-7; dihydroergocristine methanesulfonate, 24730-10-7; dipyridamole, 58-32-2; docetaxel, 114977-28-5; estradiol, 50-28-2; fluindostatin, 93957-54-1; fosinopril, 88889-14-9, 98048-97-6; losartan, 114798-26-4; mifepristone, 84371-65-3; nicardipine, 54527-84-3, 55985-32-5; olmesartan, 144689-63-4; pranlukast, 103177-37-3; pyrantel embonate, 22204-24-6; rapamycin, 53123-88-9; rifampicin, 13292-46-1; rifamycin, 6998-60-3, 14897-39-3, 15105-92-7; salazosulfapyridine, 599-79-1; suramin, 129-46-4, 145-63-1; telmisartan, 144701-48-4; tibolone, 5630-53-5; troglitazone, 97322-87-7
Harm T. Jansen - One of the best experts on this subject based on the ideXlab platform.
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Generation of Bayesian prediction models for OATP-mediated drug–drug interactions based on inhibition screen of OATP1B1, OATP1B1∗15 and OATP1B3
European Journal of Pharmaceutical Sciences, 2015Co-Authors: Evita Van De Steeg, Jennifer Venhorst, Harm T. Jansen, I.h.g. Nooijen, Heleen M. Wortelboer, Jack Degroot, Maria L. H. VlamingAbstract:Human organic anion-transporting polypeptide 1B1 (OATP1B1) and OATP1B3 are important hepatic uptake transporters. Early assessment of OATP1B1/1B3-mediated drug-drug interactions (DDIs) is therefore important for successful drug development. A promising approach for early screening and prediction of DDIs is computational modeling. In this study we aimed to generate a rapid, single Bayesian prediction model for OATP1B1, OATP1B1∗15 and OATP1B3 inhibition. Besides our previously generated HEK-OATP1B1 and HEK-OATP1B1∗15 cells, we now generated and characterized HEK-OATP1B3 cells. Using these cell lines we investigated the inhibitory potential of 640 FDA-approved drugs from a commercial library (10 μM) on the uptake of [3H]-estradiol-17β-d-glucuronide (1 μM) by OATP1B1, OATP1B1∗15, and OATP1B3. Using a cut-off of ≥60% inhibition, 8% and 7% of the 640 drugs were potent OATP1B1 and OATP1B1∗15 inhibitors, respectively. Only 1% of the tested drugs significantly inhibited OATP1B3, which was not sufficient for Bayesian modeling. Modeling of OATP1B1 and OATP1B1∗15 inhibition revealed that presence of conjugated systems and (hetero)cycles with acceptor/donor atoms in- or outside the ring enhance the probability of a molecule binding these transporters. The overall performance of the model for OATP1B1 and OATP1B1∗15 was ≥80%, including evaluation with a true external test set. Our Bayesian classification model thus represents a fast, inexpensive and robust means of assessing potential binding of new chemical entities to OATP1B1 and OATP1B1∗15. As such, this model may be used to rank compounds early in the drug development process, helping to avoid adverse effects in a later stage due to inhibition of OATP1B1 and/or OATP1B1∗15. Chemicals/CAS: abamectin, 71751-41-2; acemetacin, 53164-05-9; atazanavir, 198904-31-3; Bromocriptine Mesilate, 22260-51-1; clarithromycin, 81103-11-9; clobetasol propionate, 25122-46-7; dihydroergocristine methanesulfonate, 24730-10-7; dipyridamole, 58-32-2; docetaxel, 114977-28-5; estradiol, 50-28-2; fluindostatin, 93957-54-1; fosinopril, 88889-14-9, 98048-97-6; losartan, 114798-26-4; mifepristone, 84371-65-3; nicardipine, 54527-84-3, 55985-32-5; olmesartan, 144689-63-4; pranlukast, 103177-37-3; pyrantel embonate, 22204-24-6; rapamycin, 53123-88-9; rifampicin, 13292-46-1; rifamycin, 6998-60-3, 14897-39-3, 15105-92-7; salazosulfapyridine, 599-79-1; suramin, 129-46-4, 145-63-1; telmisartan, 144701-48-4; tibolone, 5630-53-5; troglitazone, 97322-87-7
