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Noncompartmental analysis (NCA) is a model‐independent approach for assessing pharmacokinetics (PKs). Although the existing NCA algorithms are very well‐established and widely utilized, they suffer from low accuracies in the setting of sparse PK samples. In response, we developed Deep‐NCA, a deep learning (DL) model to improve the prediction of key noncompartmental PK parameters. Our methodology utilizes synthetic PK data for model training and uses an innovative patient‐specific normalization method for data preprocessing. Deep‐NCA demonstrated adequate performance across six previously unseen simulated drugs under multiple dosing, showcasing effective generalization. Compared to traditional NCA, Deep‐NCA exhibited superior performance for sparse PK data. This study advances the application of DL to PK studies and introduces an effective method for handling sparse PK data. With further validation and refinement, Deep‐NCA could significantly enhance the efficiency of drug development by providing more accurate NCA estimates while requiring fewer PK samples. [ABSTRACT FROM AUTHOR]

Nilotinib, a second‐generation tyrosine kinase inhibitor (TKI), has been approved in the United States and Europe as a treatment for patients with newly diagnosed chronic myeloid leukemia (CML)‐chronic phase (CP) and patients with CML‐CP or chronic myeloid leukemia‐accelerated phase (CML‐AP) who are resistant or intolerant to imatinib (a first‐generation TKI). This study compared the bioequivalence and safety of the test nilotinib capsule and reference nilotinib capsule (Tasigna, Novartis) in healthy Chinese volunteers under fasting conditions for marketing authorization in China. The results of the study are reported for the first time. This was a single‐dose, randomized, open‐label, two‐period, and cross‐over study. Thirty healthy volunteers were randomly assigned to receive a single dose of a 200‐mg test or reference capsule under fasting conditions in each period with a 10‐day washout. Plasma samples were analyzed with liquid chromatography‐tandem mass spectrometry. Pharmacokinetic parameters were calculated with WinNonlin software. The geometric mean ratio and the corresponding 90% confidence intervals of Cmax, AUC0‐t, and AUC0‐∞ for nilotinib between the two fixed‐dose combination formulations were within the bioequivalence acceptance range of 80%–125%, therefore the generic and branded formulations were bioequivalent in healthy Chinese volunteers. [ABSTRACT FROM AUTHOR]

Prediction of human pharmacokinetics (PK) based on preclinical information for antibody–drug conjugates (ADCs) provide important insight into first‐in‐human (FIH) study design. This retrospective analysis was conducted to identify an appropriate scaling method to predict human PK for ADCs from animal PK data in the linear range. Different methods for projecting human clearance (CL) from animal PK data for 11 ADCs exhibiting linear PK over the tested dose ranges were examined: multiple species allometric scaling (CL vs. body weight), allometric scaling with correction factors, allometric scaling based on rule of exponent, and scaling from only cynomolgus monkey PK data. Two analytes of interest for ADCs, namely total antibody and conjugate (measured as conjugated drug or conjugated antibody), were assessed. Percentage prediction errors (PEs) and residual sum of squares (RSS) were compared across methods. Human CL was best estimated using cynomolgus monkey PK data alone and an allometric scaling exponent of 1.0 for CL. This was consistently observed for both conjugate and total antibody analytes. Other scaling methods either underestimated or overestimated human CL, or produced larger average absolute PEs and RSS. Human concentration‐time profiles were also reasonably predicted from the cynomolgus monkey data using species‐invariant time method with a fixed exponent of 1.0 for CL and 1.0 for volume of distribution. In conclusion, results from this retrospective analysis of 11 ADCs indicate that allometric scaling of CL with an exponent of 1.0 using cynomolgus monkey PK data alone can successfully project human PK profiles of an ADC within linear range. [ABSTRACT FROM AUTHOR]

Abstract: A rapid and precise liquid chromatography coupled with hybrid ion trap/time‐of‐flight mass spectrometry method to detect and quantify caulophine and its possible active metabolites in rat plasma and urine was developed. Samples were prepared by plasma protein precipitation combined with a liquid‐liquid extraction method. The separation was carried out on an InertSustain® C18 column with a mobile phase comprising methanol and 0.1% aqueous formic acid solution. The analysis was complete in 20 min with a flow rate of 0.4 mL/min. Taspine was used as the internal standard. Mass spectrometric detection was conducted with hybrid ion trap/time‐of‐flight equipped with electrospray ionization in the positive ion mode. The calibration curves of caulophine were linear over the concentration ranges of 0.002–0.20 μg/mL for plasma and 0.005–0.50 μg/mL for urine with the correlation coefficients greater than 0.998 in both cases. The method was successfully used to investigate the pharmacokinetics and bioavailability in rat plasma and urine samples after intragastric and intraperitoneal administration of caulophine sodium salt. [ABSTRACT FROM AUTHOR]