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Natural language processing (NLP) is a branch of artificial intelligence, which combines computational linguistics, machine learning, and deep learning models to process human language. Although there is a surge in NLP usage across various industries in recent years, NLP has not been widely evaluated and utilized to support drug development. To demonstrate how advanced NLP can expedite the extraction and analyses of information to help address clinical pharmacology questions, inform clinical trial designs, and support drug development, three use cases are described in this article: (1) dose optimization strategy in oncology, (2) common covariates on pharmacokinetic (PK) parameters in oncology, and (3) physiologically‐based PK (PBPK) analyses for regulatory review and product label. The NLP workflow includes (1) preparation of source files, (2) NLP model building, and (3) automation of data extraction. The Clinical Pharmacology and Biopharmaceutics Summary Basis of Approval (SBA) documents, US package inserts (USPI), and approval letters from the US Food and Drug Administration (FDA) were used as our source data. As demonstrated in the three example use cases, advanced NLP can expedite the extraction and analyses of large amounts of information from regulatory review documents to help address important clinical pharmacology questions. Although this has not been adopted widely, integrating advanced NLP into the clinical pharmacology workflow can increase efficiency in extracting impactful information to advance drug development. [ABSTRACT FROM AUTHOR]

The role of P‐glycoprotein (P‐gp) and breast cancer resistance protein (BCRP) in drug–drug interactions (DDIs) and limiting drug absorption as well as restricting the brain penetration of drugs with certain physicochemical properties is well known. P‐gp/BCRP inhibition by drugs in the gut has been reported to increase the systemic exposure to substrate drugs. A previous International Transporter Consortium (ITC) perspective discussed the feasibility of P‐gp/BCRP inhibition at the blood–brain barrier and its implications. This ITC perspective elaborates and discusses specifically the hepatic and renal P‐gp/BCRP (referred as systemic) inhibition of drugs and whether there is any consequence for substrate drug disposition. This perspective summarizes the clinical evidence‐based recommendations regarding systemic P‐gp and BCRP inhibition of drugs with a focus on biliary and active renal excretion pathways. Approaches to assess the clinical relevance of systemic P‐gp and BCRP inhibition in the liver and kidneys included (i) curation of DDIs involving intravenously administered substrates or inhibitors; (ii) in vitro‐to‐in vivo extrapolation of P‐gp‐mediated DDIs at the systemic level; and (iii) curation of drugs with information available about the contribution of biliary excretion and related DDIs. Based on the totality of evidence reported to date, this perspective supports limited clinical DDI risk upon P‐gp or BCRP inhibition in the liver or kidneys. [ABSTRACT FROM AUTHOR]

Mechanistic understanding of complex clinical drug–drug interactions (DDIs) with potential involvement of multiple elimination pathways has been challenging, especially given the general lack of specific probe substrates for transporters. Here, we conducted a clinical DDI study to evaluate the interaction potential of fenebrutinib using midazolam (MDZ; CYP3A), simvastatin (CYP3A and OATP1B), and rosuvastatin (BCRP and OATP1B) as probe substrates. Fenebrutinib (200 mg) increased the area under the curve (AUC) of these probe substrates twofold to threefold. To evaluate the mechanism of the observed DDIs, we measured the concentration of coproporphyrin I (CP‐I) and coproporphyrin III (CP‐III), endogenous biomarkers of OATP1B. There was no change in CP‐I or CP‐III levels with fenebrutinib, suggesting that the observed DDIs were caused by inhibition of CYP3A and BCRP rather than OATP1B, likely due to increased bioavailability. This is the first published account using an endogenous transporter biomarker to understand the mechanism of complex DDIs involving multiple elimination pathways. [ABSTRACT FROM AUTHOR]

Drug transporters can govern the absorption, distribution, metabolism, and excretion of substrate drugs and endogenous substances. Investigations to examine their potential impact to pharmacokinetic (PK) drug‐drug interactions (DDIs) are an integral part of the risk assessment in drug development. To evaluate a new molecular entity as a potential perpetrator of transporters, use of well characterized and/or clinically relevant probe substrates with good selectivity and sensitivity are critical for robust clinical DDI assessment that could inform DDI management strategy in the product labeling. The availability of endogenous biomarkers to monitor transporter‐mediated DDIs in early phases of clinical investigations would greatly benefit downstream clinical plans. This article reviews the state‐of‐the‐art in transporter clinical probe drugs and emerging biomarkers, including current challenges and limitations, delineates methods and workflows to identify and validate novel endogenous biomarkers to support clinical DDI evaluations, and proposes how these probe drugs or biomarkers could be used in drug development. [ABSTRACT FROM AUTHOR]

Our recent studies have shown that chronic kidney disease (CKD) affects the pharmacokinetics (PKs) of cytochrome P450 (CYP)2D6‐metabolized drugs, whereas effects were less evident on CYP3A4/5. Therefore, the effect of CKD on the disposition of CYP1A2‐metabolized, CYP2C8‐metabolized, CYP2C9‐metabolized, CYP2C19‐metabolized, and organic anion‐transporting polypeptide (OATP)‐transported drugs was investigated. We identified dedicated CKD studies with 6, 5, 6, 4, and 12 “model” substrates for CYP1A2, CYP2C8, CYP2C9, CYP2C19, and OATP, respectively. Our analyses suggest that clearance of OATP substrates decreases as kidney function declines. Similar trends were seen for CYP2C8; but overlap between some CYP2C8 and OATP substrates highlights that their interplay needs further investigation. In contrast, the effect of CKD on CYP1A2, CYP2C9, and CYP2C19 was variable and modest compared to CYP2C8 and OATP. This improved understanding of elimination‐pathway‐dependency in CKD is important to inform the need and conduct of PK studies in these patients for nonrenally eliminated drugs. [ABSTRACT FROM AUTHOR]