Mass Balance, Metabolic Pathways, Absolute Bioavailability, And Pharmacokinetics of Giredestrant In Healthy Subjects.

Giredestrant is a potent and selective small molecule estrogen receptor degrader. The objectives of this study were to assess the absolute bioavailability (aBA) of giredestrant and to determine the mass balance, routes of elimination and metabolite profile of [14C]giredestrant. In Part 1 (mass balance), a single 30.8 mg oral dose of [14C]giredestrant (105 µCi) was administered to women of non-childbearing potential (WNCBP, n = 6). The mean recovery of total radioactivity (TR) in excreta was 77.0%, with 68.0% of the dose excreted in feces and 9.04% excreted in urine over a 42-day sample collection period. The majority of the circulating radioactivity (56.8%) in plasma was associated with giredestrant. Giredestrant was extensively metabolized with giredestrant representing only 20.0% and 1.90% of the dose in feces and urine, respectively. All metabolites in feces resulted from oxidative metabolism and represented 44.7% of the dose. In Part 2 (absolute bioavailability, aBA), WNCBP (n = 10) received an oral (30 mg capsule) or intravenous (30 mg solution) dose of giredestrant. The aBA of giredestrant after oral administration was 58.7%. Following the intravenous dose, giredestrant had a plasma clearance and volume of distribution of 5.31 L/h and 266 L, respectively. In summary, giredestrant was well tolerated, rapidly absorbed, and showed moderate oral bioavailability with low recovery of the dose as parent drug in excreta. Oxidative metabolism followed by excretion in feces was identified as the major route of elimination of giredestrant. Significance Statement This study provides definitive insight into the absorption, distribution, metabolism, and excretion of giredestrant in humans. The results show that giredestrant exhibits low clearance, high volume of distribution, and moderate oral bioavailability in humans. In addition, the data show that oxidative metabolism followed by excretion in feces is the primary elimination route of giredestrant in humans. These results will be used to further inform the clinical development of giredestrant.

Pharmacokinetics and Metabolism of Melflufen, an Alkylating Peptide-Drug Conjugate, in Patients with Relapsed Refractory Multiple Myeloma.

Melphalan flufenamide (melflufen) is a novel lipophilic peptide-drug conjugate recently approved in the European Union and the United Kingdom for the treatment of relapsed refractory multiple myeloma. Melflufen rapidly crosses the cell membrane, and inside tumor cells, melflufen utilizes peptidases and esterases to release entrapped hydrophilic metabolites with alkylating activity. In vitro, in whole blood, melflufen was rapidly distributed into blood cells and quickly converted to its main metabolite melphalan, with maximum cellular concentrations of noncovalently bound melflufen and melphalan after 1 and 6 minutes, respectively. Melphalan outflow from blood cells was slow, with peak concentrations in plasma after 25 minutes. The pharmacokinetics of melflufen was best described by a 2-compartment model. Following a 30-minutes intravenous infusion of 40 mg in 27 patients with relapsed refactory multiple myeloma, mean half-life in the α phase of the curve was 1.24 minutes, half-life in the ß phase of the curve 26.7 minutes, and clearance 13.4 L/min. Desethyl-melflufen exposure was below 20% compared to melflufen. Based on population analysis (298 patients with relapsed refactory multiple myeloma), the melphalan pharmacokinetics were well characterized by a 3-compartment model with melflufen dosing into a peripheral compartment, assuming instantaneous distribution of melflufen into cells and subsequent rapid metabolism to melphalan. Mean clearance and central and deep peripheral volumes of distribution were 22.4 L/h, 2.70 L, and 51.3 L, respectively. Clearance increased and maximum concentration decreased with increasing body weight and estimated glomerular filtration rate. In conclusion, melflufen administration differs from melphalan administration by a more rapid distribution into cells, which, in conjunction with a rapid intracellular metabolism, allows for higher maximum concentrations of alkylating agents, and by a more extensive distribution of melphalan to peripheral tissues.

Applications of Advanced Natural Language Processing for Clinical Pharmacology.

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.

Differences in Glucose Readings Between Right Arm and Left Arm Using a Continuous Glucose Monitor.

OBJECTIVE: Continuous glucose monitoring (CGM) devices are used for evaluating real-time glucose levels to optimize diabetes management. There is limited information, however, on whether readings differ when a device is placed on the right versus the left arm. This study evaluated the mean difference in glucose levels between the right and left arm and the effect of unilateral arm exercise on this difference. The effect of an intermittent fasting diet on body fat percentage was also evaluated. RESEARCH DESIGN AND METHODS: In a prospective trial, 46 adult volunteers self-selected into the intermittent fasting (IF; N = 23) or free-living (FL; N = 23) diet group and were randomized into a unilateral arm exercise group. Volunteers had CGM sensors placed simultaneously on both arms for 12-14 days. RESULTS: The mean glucose level in the right arm was significantly higher than the left arm by 3.7 mg/dL (P < .001), and this result was unaffected by diet or arm exercise. Glucose levels were in euglycemic range for 75.2% of the time in the right arm and 67.5% in the left arm (P < .001). The change from baseline in body fat percentage between the IF and FL diet groups was not significant. CONCLUSIONS: Measured glucose level and time in euglycemic range differ per placement of the CGM device, and the implications of this difference should be considered in clinical practice and research.

A longitudinal model for the Mayo Clinical Score and its sub-components in patients with ulcerative colitis.

Clinical trials in patients with ulcerative colitis (UC) face the challenge of high and variable placebo response rates. The Mayo Clinical Score (MCS) is used widely as the primary endpoint in clinical trials to describe the clinical status of patients with UC. The MCS is comprised of four subscores, each scored 0, 1, 2 and 3: rectal bleeding (RB), stool frequency (SF), physician's global assessment (PGA), and endoscopy (ENDO) subscore. Excluding the PGA subscore gives the modified MCS. Quantitative insight on the placebo response, and its impact on the components of the MCS over time, can better inform clinical trial design and interpretation. Longitudinal modeling of the MCS, and the modified MCS, can be challenging due to complex clinical trial design, population heterogeneity, and limited assessments for the ENDO subscore. The current study pooled patient-level placebo/standard of care (SoC) arm data from five clinical trials in the TransCelerate database to develop a longitudinal placebo response model that describes the MCS over time in patients with UC. MCS subscores were modeled using proportional odds models, and the removal of patients from the placebo/SoC arm, or "dropout", was modeled using logistic regression models. The subscore and dropout models were linked to allow for the prediction of the MCS and the modified MCS. Stepwise covariate modeling identified prior exposure to TNF-α antagonists as a statistically significant predictor on the RB + SF subscore. Patients with prior exposure to TNF-α antagonists had higher post-baseline RB + SF subscores than naive patients.

Confounding factors in exposure-response analyses and mitigation strategies for monoclonal antibodies in oncology.

Dose selection and optimization is an important topic in drug development to maximize treatment benefits for all patients. While exposure-response (E-R) analysis is a useful method to inform dose-selection strategy, in oncology, special considerations for prognostic factors are needed due to their potential to confound the E-R analysis for monoclonal antibodies. The current review focuses on 3 different approaches to mitigate the confounding effects for monoclonal antibodies in oncology: (i) Cox-proportional hazards modelling and case-matching; (ii) tumour growth inhibition-overall survival modelling; and (iii) multiple dose level study design. In the presence of confounding effects, studying multiple dose levels may be required to reveal the true E-R relationship. However, it is impractical for pivotal trials in oncology drug development programmes. Therefore, the strengths and weaknesses of the other 2 approaches are considered, and the favourable utility of tumour growth inhibition-overall survival modelling to address confounding in E-R analyses is described. In the broader scope of oncology drug development, this review discusses the downfall of the current emphasis on E-R analyses using data from single dose level trials and proposes that development programmes be designed to study more dose levels in earlier trials.

Population pharmacokinetic analysis of high-dose methotrexate in pediatric and adult oncology patients.

PURPOSE: High-dose methotrexate (HD-MTX) is widely used in pediatric and adult oncology treatment regimens. This study aimed to develop a population pharmacokinetic model to characterize pediatric and adult MTX exposure across various disease types and dosing regimens, and to evaluate exposure-toxicity relationships. METHODS: MTX pharmacokinetic data from pediatric and adult patients were collected. A population pharmacokinetic model was developed to determine the effects of age, liver function, renal function, and demographics on MTX disposition. The final model was used in Monte Carlo simulations to generate expected exposures for different dosing regimens. The association of toxicity, determined through chart review, and MTX area under the curve (AUC) was modeled using logistic regression. RESULTS: The analysis included 5116 MTX concentrations from 320 patients (135 adult, age 19-79 years; 185 pediatric, age 0.6-19 years). Estimated glomerular filtration rate (eGFR) and treatment cycle number were independent predictors of clearance (CL). CL varied 2.1-fold over the range of study eGFR values and increased 14% for treatment cycle numbers greater than 7. Higher MTX AUC was associated with higher risk of nephrotoxicity in adults, and neurotoxicity and hepatotoxicity in pediatrics. CONCLUSIONS: This study represents one of the most comprehensive evaluations of HD-MTX PK across a wide range of ages and disease types. After accounting for differences in renal function, age did not impact CL, although toxicity patterns differed by age. The model allows for early identification of patients with slowed MTX clearance and at higher risk of toxicity.