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Contribution of classical complement activation and IgM to the control of Rickettsia infection

Molecular microbiology, 2021-12, Vol.116 (6), p.1476-1488

2021

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Breast cancer resistance protein (ABCG2) in clinical pharmacokinetics and drug interactions: practical recommendations for clinical victim and perpetrator drug-drug interaction study design

Drug metabolism and disposition, 2015-04, Vol.43 (4), p.490-509

2015

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Model-Informed Precision Dosing: Background, Requirements, Validation, Implementation, and Forward Trajectory of Individualizing Drug Therapy

Annual review of pharmacology and toxicology, 2021-01, Vol.61 (1), p.225-245

2021

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Exposure‐Response Characterization of Tofacitinib Efficacy in Moderate to Severe Ulcerative Colitis: Results From Phase II and Phase III Induction and Maintenance Studies

Clinical pharmacology and therapeutics, 2022-07, Vol.112 (1), p.90-100

2022

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Ensuring diversity in clinical trials: The role of clinical pharmacology

Contemporary clinical trials, 2022-07, Vol.118, p.106807-106807, Article 106807

2022

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Anaphylatoxin signaling activates macrophages to control intracellular Rickettsia proliferation

Microbiology spectrum, 2023-12, Vol.11 (6), p.e0253823-e0253823

2023

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ITC Commentary on Metformin Clinical Drug–Drug Interaction Study Design That Enables an Efficacy‐ and Safety‐Based Dose Adjustment Decision

Clinical pharmacology and therapeutics, 2018-11, Vol.104 (5), p.781-784

2018

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Correction to: Integrated Multi-stakeholder Systems Thinking Strategy: Decision-making with Biopharmaceutics Risk Assessment Roadmap (BioRAM) to Optimize Clinical Performance of Drug Products

The AAPS journal, 2020-12, Vol.23 (1), p.7-7, Article 7

2020

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Comparison of different algorithms for predicting clinical drug-drug interactions, based on the use of CYP3A4 in vitro data: predictions of compounds as precipitants of interaction

Drug metabolism and disposition, 2009-08, Vol.37 (8), p.1658-1666

2009

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Integrated Multi-stakeholder Systems Thinking Strategy: Decision-making with Biopharmaceutics Risk Assessment Roadmap (BioRAM) to Optimize Clinical Performance of Drug Products

The AAPS journal, 2020-07, Vol.22 (5), p.97-97, Article 97

2020

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A Technique to Estimate In Vivo Dissolution Profiles Without Data from a Solution

The AAPS journal, 2012-09, Vol.14 (3), p.433-436

Cook, Jack A.

2012

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Development strategies for IVIVC in an industrial environment

Biopharmaceutics & drug disposition, 2012-10, Vol.33 (7), p.349-353

Cook, Jack A.

2012

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A proposal for scientific framework enabling specific population drug dosing recommendations

Journal of clinical pharmacology, 2015-10, Vol.55 (10), p.1073-1078

2015

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Application of the Biopharmaceutical Classification System in Clinical Drug Development—An Industrial View

The AAPS journal, 2008-06, Vol.10 (2), p.306-310

2008

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The Biopharmaceutics Risk Assessment Roadmap for Optimizing Clinical Drug Product Performance

Journal of pharmaceutical sciences, 2014-11, Vol.103 (11), p.3377-3397

2014

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P-glycoprotein related drug interactions: clinical importance and a consideration of disease states

Expert opinion on drug metabolism & toxicology, 2010-05, Vol.6 (5), p.603-619

2010

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Optimizing Clinical Drug Product Performance: Applying Biopharmaceutics Risk Assessment Roadmap (BioRAM) and the BioRAM Scoring Grid

Journal of pharmaceutical sciences, 2016-11, Vol.105 (11), p.3243-3255

2016

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Driving Access to Medicines With a Totality of Evidence Mindset: An Opportunity for Clinical Pharmacology

Clinical pharmacology and therapeutics, 2018-03, Vol.103 (3), p.373-375

2018

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To blind or not to blind first in human and exploratory clinical trials: Acceleration of development vs. risk of bias

Clinical and translational science, 2022-03, Vol.15 (3), p.601-609

2022

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Impact of Biopharmaceutics Classification System-Based Biowaivers

Molecular pharmaceutics, 2010-10, Vol.7 (5), p.1539-1544

2010

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The Weight of Evidence From Electrophysiology, Observational, and Cardiovascular End Point Studies Demonstrates the Safety of Azithromycin

Clinical and translational science, 2021-01, Vol.14 (1), p.106-112

2021

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Building Intelligent Systems for Paying Healthcare Providers and Using Social Media to Detect Fraudulent Claims

International journal of organizational and collective intelligence, 2017-04, Vol.7 (2), p.13-33

2017

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Pharmacokinetics of Pregabalin in Subjects with Various Degrees of Renal Function

Journal of clinical pharmacology, 2003-03, Vol.43 (3), p.277-283

2003

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Refining the In Vitro and In Vivo Critical Parameters for P-Glycoprotein, [I]/IC50 and [I2]/IC50, That Allow for the Exclusion of Drug Candidates from Clinical Digoxin Interaction Studies

Molecular pharmaceutics, 2010-04, Vol.7 (2), p.398-411

2010

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Application of CYP3A4 in vitro data to predict clinical drug–drug interactions; predictions of compounds as objects of interaction

British journal of clinical pharmacology, 2008-05, Vol.65 (5), p.680-692

2008

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Contribution of classical complement activation and IgM to the control of Rickettsia infection

Breast cancer resistance protein (ABCG2) in clinical pharmacokinetics and drug interactions: practical recommendations for clinical victim and perpetrator drug-drug interaction study design

Model-Informed Precision Dosing: Background, Requirements, Validation, Implementation, and Forward Trajectory of Individualizing Drug Therapy

Exposure‐Response Characterization of Tofacitinib Efficacy in Moderate to Severe Ulcerative Colitis: Results From Phase II and Phase III Induction and Maintenance Studies

Ensuring diversity in clinical trials: The role of clinical pharmacology

Anaphylatoxin signaling activates macrophages to control intracellular Rickettsia proliferation

ITC Commentary on Metformin Clinical Drug–Drug Interaction Study Design That Enables an Efficacy‐ and Safety‐Based Dose Adjustment Decision

Correction to: Integrated Multi-stakeholder Systems Thinking Strategy: Decision-making with Biopharmaceutics Risk Assessment Roadmap (BioRAM) to Optimize Clinical Performance of Drug Products

Comparison of different algorithms for predicting clinical drug-drug interactions, based on the use of CYP3A4 in vitro data: predictions of compounds as precipitants of interaction

Integrated Multi-stakeholder Systems Thinking Strategy: Decision-making with Biopharmaceutics Risk Assessment Roadmap (BioRAM) to Optimize Clinical Performance of Drug Products

A Technique to Estimate In Vivo Dissolution Profiles Without Data from a Solution

Development strategies for IVIVC in an industrial environment

A proposal for scientific framework enabling specific population drug dosing recommendations

Application of the Biopharmaceutical Classification System in Clinical Drug Development—An Industrial View

The Biopharmaceutics Risk Assessment Roadmap for Optimizing Clinical Drug Product Performance

P-glycoprotein related drug interactions: clinical importance and a consideration of disease states

Optimizing Clinical Drug Product Performance: Applying Biopharmaceutics Risk Assessment Roadmap (BioRAM) and the BioRAM Scoring Grid

Driving Access to Medicines With a Totality of Evidence Mindset: An Opportunity for Clinical Pharmacology

To blind or not to blind first in human and exploratory clinical trials: Acceleration of development vs. risk of bias

Impact of Biopharmaceutics Classification System-Based Biowaivers

The Weight of Evidence From Electrophysiology, Observational, and Cardiovascular End Point Studies Demonstrates the Safety of Azithromycin

Building Intelligent Systems for Paying Healthcare Providers and Using Social Media to Detect Fraudulent Claims

Pharmacokinetics of Pregabalin in Subjects with Various Degrees of Renal Function

Refining the In Vitro and In Vivo Critical Parameters for P-Glycoprotein, [I]/IC50 and [I2]/IC50, That Allow for the Exclusion of Drug Candidates from Clinical Digoxin Interaction Studies

Application of CYP3A4 in vitro data to predict clinical drug–drug interactions; predictions of compounds as objects of interaction

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