Pharmacokinetic Metrics Research Articles

Characterisation of the pharmacokinetic and pharmacodynamic relationship of milvexian in total knee replacement patients

Abstract Background Milvexian, an oral factor XIa inhibitor, is currently being evaluated in patients at risk for thrombotic events due to atrial fibrillation, ischaemic stroke, or acute coronary syndrome. The exposure-response relationship between clinical efficacy and safety endpoints and milvexian pharmacokinetic (PK) metrics was recently submitted as a separate publication. Characterisation of the relationship between the coagulation pharmacodynamic (PD) markers versus milvexian PK metrics at the patient level has yet to be reported. Purpose This analysis characterised the relationship between the coagulation biomarkers, activated partial thromboplastin time (aPTT) and prothrombin time (PT), and milvexian systemic exposure in patients undergoing total knee replacement (TKR) evaluated in the AXIOMATIC-TKR study. Methods A population pharmacokinetic (PopPK) model was previously developed and characterised the PK of milvexian in TKR patients well. The PK/PD relationships between observed aPTT and PT versus PopPK model estimated time-matched milvexian plasma concentrations via nonlinear mixed effects modeling. The relationship between key clinical safety endpoints (e.g., any bleeding or clinically relevant non-major bleeding) and individual PD marker change (e.g., aPTT ratio to baseline) was also explored. Results The aPTT increased with increasing plasma concentration of milvexian over the range of doses evaluated in the study (25mg QD to 200mg BID). An Emax type of model reasonably characterised the PK/PD relationship between the aPTT and model predicted time-matched milvexian plasma concentration, which is consistent with previously observed relationships in healthy volunteers. No change was observed in PT with increased milvexian exposure. No obvious relationship was identified between any (including clinically relevant non-major) bleeding and aPTT ratio to baseline. Conclusions Increased milvexian exposure is directly correlated with aPTT prolongation over the range of doses evaluated in the TKR study. There was no obvious relationship between any (including clinically relevant non-major) bleeding and aPTT ratio. The PK/PD relationship of aPTT without a parallel relationship in PT distinguishes milvexian, a FXIa inhibitor, from previously studied FXa inhibitors. These analyses were a complementary component in dose selection for the milvexian Phase 3 program. Figure 1. PK/PD relationship between aPTT (left) and PT (right) and estimated time-matched plasma concentration (AXIOMATIC-TKR study)

Therapeutic Drug Monitoring of Imatinib: Is There a Rationale for Also Quantifying Its Active Metabolite?

Introduction: Therapeutic drug monitoring of imatinib is widely performed to individualize imatinib dosage. While N-desmethyl imatinib is an active metabolite of imatinib, its concentrations are not routinely determined. Methods: Imatinib and N-desmethyl imatinib trough plasma concentrations at steady-state were obtained from 295 patients with either chronic myeloid leukemia or gastrointestinal stromal tumor to see whether N-desmethyl imatinib provided additional information. Pharmacokinetic data were analyzed using a nonlinear mixed effect approach. Correlations between several pharmacokinetic metrics of drug exposure were evaluated. Results: The mean value of the N-desmethyl imatinib/imatinib ratio of trough concentrations was 0.31 with half of the values between 0.23 and 0.37. N-desmethyl imatinib and total (i.e., N-desmethyl imatinib plus imatinib) trough plasma concentrations or area under the curve values were closely correlated with imatinib values. The distribution of imatinib or total concentrations between patients requiring imatinib dosage adjustment, or not, was similar. Conclusion: These results do not clearly support routine N-desmethyl monitoring since it does not provide additional information to imatinib data.

Development and comparison of model-integrated evidence approaches for bioequivalence studies with pharmacokinetic end points.

By applying nonlinear mixed-effect (NLME) models, model-integrated evidence (MIE) approaches are able to analyze bioequivalence (BE) data with pharmacokinetic end points that have sparse sampling, which is problematic for non-compartmental analysis (NCA). However, MIE approaches may suffer from inflation of type I error due to underestimation of parameter uncertainty and to the assumption of asymptotic normality. In this study, we developed a MIE BE analysis method that is based on a pre-defined model and consists of several steps including model fitting, uncertainty assessment, simulation, and BE determination. The presented MIE approach has several improvements compared with the previously reported model-integrated methods: (1) treatment, sequence, and period effects are only added to absorption parameters (such as relative bioavailability and rate of absorption) instead of all PK parameters; (2) a simulation step is performed to generate confidence intervals of the pharmacokinetic metrics for BE assessment; and (3) in an effort to maintain type I error, two more advanced parameter uncertainty evaluation approaches are explored, a nonparametric (case resampling) bootstrap, and sampling importance resampling (SIR). To evaluate the developed method and compare the uncertainty assessment methods, simulation experiments were performed for BE studies using a two-way crossover design with different amounts of information (sparse to rich designs) and levels of variability. Based on the simulation results, the method using SIR for parameter uncertainty quantification controls type I error at the nominal level of 0.05 (i.e., the significance level set for BE evaluation) even for studies with small sample size and/or sparse sampling. As expected, our MIE approach for BE assessment exhibited higher power than the NCA-based method, especially as the data becomes sparser and/or more variable.

Clustering plasma concentration-time curves: applications ofunsupervised learning in pharmacogenomics

ABSTRACT Pharmaceutical researchers are continually searching for techniques to improve both drug development processes and patient outcomes. An area of recent interest is the potential for machine learning (ML) applications within pharmacology. One such application not yet given close study is the unsupervised clustering of plasma concentration-time curves, hereafter, pharmacokinetic (PK) curves. In this paper, we present our findings on how to cluster PK curves by their similarity. Specifically, we find clustering to be effective at identifying similar-shaped PK curves and informative for understanding patterns within each cluster of PK curves. Because PK curves are time series data objects, our approach utilizes the extensive body of research related to the clustering of time series data as a starting point. As such, we examine many dissimilarity measures between time series data objects to find those most suitable for PK curves. We identify Euclidean distance as generally most appropriate for clustering PK curves, and we further show that dynamic time warping, Fréchet, and structure-based measures of dissimilarity like correlation may produce unexpected results. As an illustration, we apply these methods in a case study with 250 PK curves used in a previous pharmacogenomic study. Our case study finds that an unsupervised ML clustering with Euclidean distance, without any subject genetic information, is able to independently validate the same conclusions as the reference pharmacogenomic results. To our knowledge, this is the first such demonstration. Further, the case study demonstrates how the clustering of PK curves may generate insights that could be difficult to perceive solely with population level summary statistics of PK metrics.

2-Deoxyglucose and hydroxychloroquine HPLC-MS-MS analytical methods and pharmacokinetic interactions after oralco-administration in male rats.

Our previous work has shown a synergistic tumoricidal efficacy of combining the hexokinase (HK) inhibitor 2-deoxyglucose (2-DG) and the autophagy inhibitor chloroquine (CQ) through intraperitoneal injections on HK2-addicted prostate cancers in animal models. The pharmacokinetic (PK) behaviors of these oral drugs after simultaneous oral administration have not been reported. We developed high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS-MS) analytical methods for 2-DG and the clinically favored drug hydroxychloroquine (HCQ) for sera samples. Using a jugular vein-cannulated male rat model with serial blood collection before and after a single gavage dose of each drug alone or in combination, we examined their PK metrics for drug-drug interactions. The data demonstrated a rapid and complete separation of 2-DG from common monosaccharides by HPLC-MS-MS multi-reaction monitoring. Application of the HPLC-MS-MS 2-DG and HCQ methods to sera samples of nine rats showed a peak time (Tmax ) for 2-DG of 0.5 h after 2-DG alone or with HCQ and identical post-peak half-life of approximately 1 h. With a seemingly bi-modal time course for HCQ, the Tmax for HCQ alone (1.2 h) was faster than that for the combination (2 h; p = .017). After combination dosing, the peak concentration (Cmax ) and area under the curve (AUC0-4h ) of 2-DG were decreased by 53.8% (p = .0004) and 53.7% (p = .0001), whereas AUC0-8h for HCQ was decreased by 30.8% (p = .0279) from the respective single dosing. Without changing the mean residence time (MRT0-∞ ) of each drug, the combination affected the apparent volume of distribution (Vd ) and clearance (CL) of 2-DG, and CL for HCQ without affecting its Vd . We observed significant negative PK interactions, probably at the intestinal absorption level, between 2-DG and HCQ taken simultaneously by mouth. Future optimization efforts are warranted for their combination regimen for clinical translation.

P133 In vitro pharmacological profile of GLPG3667 suggests differentiation from the TYK2 inhibitors deucravacitinib and TAK-279 at their clinical dose regimens

Abstract Background Tyrosine kinase 2 (TYK2), a Janus kinase (JAK) family member, is attracting a lot of interest as a new target to treat patients with autoimmune and inflammatory diseases, including inflammatory bowel disease, and several inhibitors are currently in clinical development. In vitro pharmacological profiling of TYK2 inhibitors has been employed to evaluate potency and selectivity; however, such data are best viewed in relation to clinical exposure levels. We compared expected levels of inhibition of TYK2-dependent and -independent pathways for deucravacitinib, TAK-279 and GLPG3667 at their respective clinical dose regimens and demonstrate that relevant differences exist between these compounds. Methods Potency of the TYK2 inhibitors was compared for TYK2-dependent pathways (IFNα, IL-12), TYK2-independent pathways (IL-6, IL-2, GM-CSF) and the anti-inflammatory IL-10 pathway in in vitro human whole blood assays using STAT phosphorylation (flow cytometry) and cytokine release (ELISA). Sigmoidal Emax models were fitted to the concentration–inhibition data from healthy donors to estimate IC50, Emax and Hill slope for each assay and compound. The obtained relationships were subsequently coupled to the clinical pharmacokinetic (PK) profiles of the compounds (population PK model for GLPG3667, published PK metrics such as Cavg for the other compounds) to estimate the level of inhibition of the various pathways at clinically relevant dose regimens. Results Human whole blood assays performed for various JAK pathways demonstrated that all TYK2 inhibitors display selectivity for TYK2 over the JAK1–3 family members, with TAK-279 being the most selective. At exposure levels associated with its clinical dose of 150 mg once daily, GLPG3667 showed inhibition of the IFNα and IL-12 pathways, similar to the expected inhibition for deucravacitinib at its clinical dose regimens, without a measurable impact on TYK2-independent pathways. TAK-279 showed the most sustained inhibition of TYK2-dependent pathways. GLPG3667 showed no measurable inhibition of IL-10-mediated signalling up to the highest concentration tested (~10-fold above clinical concentrations), while inhibition was observed with deucravacitinib and TAK-279 at concentrations corresponding to the respective clinical dose regimens. Conclusion At concentrations corresponding to its highest anticipated clinical dose, GLPG3667 shows selective inhibition of TYK2-mediated signalling, with a level of inhibition similar to deucravacitinib clinical dose regimens. While GLPG3667 did not show any impact on IL-10-mediated signalling, inhibition of this anti-inflammatory pathway is expected for the allosteric TYK2 inhibitors at their clinical exposure levels.

A minimal PBPK model to accelerate preclinical development of drugs against tuberculosis.

Introduction: Understanding drug exposure at disease target sites is pivotal to profiling new drug candidates in terms of tolerability and efficacy. Such quantification is particularly tedious for anti-tuberculosis (TB) compounds as the heterogeneous pulmonary microenvironment due to the infection may alter lung permeability and affect drug disposition. Murine models have been a longstanding support in TB research so far and are here used as human surrogates to unveil the distribution of several anti-TB compounds at the site-of-action via a novel and centralized PBPK design framework. Methods: As an intermediate approach between data-driven pharmacokinetic (PK) models and whole-body physiologically based (PB) PK models, we propose a parsimonious framework for PK investigation (minimal PBPK approach) that retains key physiological processes involved in TB disease, while reducing computational costs and prior knowledge requirements. By lumping together pulmonary TB-unessential organs, our minimal PBPK model counts 9 equations compared to the 36 of published full models, accelerating the simulation more than 3-folds in Matlab 2022b. Results: The model has been successfully tested and validated against 11 anti-TB compounds-rifampicin, rifapentine, pyrazinamide, ethambutol, isoniazid, moxifloxacin, delamanid, pretomanid, bedaquiline, OPC-167832, GSK2556286 - showing robust predictability power in recapitulating PK dynamics in mice. Structural inspections on the proposed design have ensured global identifiability and listed free fraction in plasma and blood-to-plasma ratio as top sensitive parameters for PK metrics. The platform-oriented implementation allows fast comparison of the compounds in terms of exposure and target attainment. Discrepancies in plasma and lung levels for the latest BPaMZ and HPMZ regimens have been analyzed in terms of their impact on preclinical experiment design and on PK/PD indices. Conclusion: The framework we developed requires limited drug- and species-specific information to reconstruct accurate PK dynamics, delivering a unified viewpoint on anti-TB drug distribution at the site-of-action and a flexible fit-for-purpose tool to accelerate model-informed drug design pipelines and facilitate translation into the clinic.

Deep learning for automatic organ and tumor segmentation in nanomedicine pharmacokinetics.

Rationale: Multimodal imaging provides important pharmacokinetic and dosimetry information during nanomedicine development and optimization. However, accurate quantitation is time-consuming, resource intensive, and requires anatomical expertise. Methods: We present NanoMASK: a 3D U-Net adapted deep learning tool capable of rapid, automatic organ segmentation of multimodal imaging data that can output key clinical dosimetry metrics without manual intervention. This model was trained on 355 manually-contoured PET/CT data volumes of mice injected with a variety of nanomaterials and imaged over 48 hours. Results: NanoMASK produced 3-dimensional contours of the heart, lungs, liver, spleen, kidneys, and tumor with high volumetric accuracy (pan-organ average %DSC of 92.5). Pharmacokinetic metrics including %ID/cc, %ID, and SUVmax achieved correlation coefficients exceeding R = 0.987 and relative mean errors below 0.2%. NanoMASK was applied to novel datasets of lipid nanoparticles and antibody-drug conjugates with a minimal drop in accuracy, illustrating its generalizability to different classes of nanomedicines. Furthermore, 20 additional auto-segmentation models were developed using training data subsets based on image modality, experimental imaging timepoint, and tumor status. These were used to explore the fundamental biases and dependencies of auto-segmentation models built on a 3D U-Net architecture, revealing significant differential impacts on organ segmentation accuracy. Conclusions: NanoMASK is an easy-to-use, adaptable tool for improving accuracy and throughput in imaging-based pharmacokinetic studies of nanomedicine. It has been made publicly available to all readers for automatic segmentation and pharmacokinetic analysis across a diverse array of nanoparticles, expediting agent development.

Trastuzumab Deruxtecan Dosing in Human Epidermal Growth Factor Receptor 2-Positive Gastric Cancer: Population Pharmacokinetic Modeling and Exposure-Response Analysis.

This study evaluated the benefit/risk of trastuzumab deruxtecan (T-DXd) 6.4mg/kg in patients with human epidermal growth factor receptor2 (HER2)-positive gastric cancer using pharmacometrics. A population pharmacokinetic (PopPK) model was developed using data from patients with gastric cancer, breast cancer, or other tumors in T-DXd clinical trials, primarily conducted in Asia. Posthoc model-estimated pharmacokinetic metrics were used in exposure-efficacy (objective response rates, ORRs) and exposure-safety analyses. The PopPK analysis included 808 patients (217 with gastric cancer, 512 with breast cancer, and 79 with other cancers). In gastric cancer, the T-DXd 6.4mg/kg steady-state exposure metrics were lower compared with 6.4mg/kg in breast cancer, but were similar to 5.4mg/kg in breast cancer. Tumor type was selected as a significant covariate on T-DXd clearance. In exposure-efficacy analysis among 160 patients with gastric cancer, the T-DXd steady-state minimum concentration was associated with a confirmed ORR in univariate logistic regression analysis (P=.023). The model-predicted confirmed ORRs in gastric cancer were 36.0% (90%CI 29.3% to 43.7%) with 5.4mg/kg and 40.0% (90%CI 33.1% to 47.6%) with 6.4mg/kg. Among 808 patients in the exposure-safety analyses, the model-predicted estimates for the rates of any-grade interstitial lung disease (ILD) over a period of 180days were 10.2% (90%CI 8.7% to 12.8%) with 6.4mg/kg in gastric cancer and 9.7% (90%CI 8.2% to 11.8%) with 5.4mg/kg in breast cancer. In gastric cancer, the efficacy of T-DXd was higher at 6.4mg/kg than at 5.4mg/kg. Exposure and ILD rates were comparable between 6.4mg/kg in gastric cancer and 5.4mg/kg in breast cancer. This study identified T-DXd 6.4mg/kg as the recommended dose in HER2-positive gastric cancer.

A PKPD Case Study: Achieving Clinically Relevant Exposures of AZD5991 in Oncology Mouse Models.

Capturing human equivalent drug exposures preclinically is a key challenge in the translational process. Motivated by the need to recapitulate the pharmacokinetic (PK) profile of the clinical stage Mcl-1 inhibitor AZD5991 in mice, we describe the methodology used to develop a refined mathematical model relating clinically relevant concentration profiles to efficacy. Administration routes were explored to achieve target exposures matching the clinical exposure of AZD5991. Intravenous infusion using vascular access button (VAB) technology was found to best reproduce clinical target exposures of AZD5991 in mice. Exposure-efficacy relationships were evaluated, demonstrating that dissimilar PK profiles result in differences in target engagement and efficacy outcomes. Thus, these data underscore the importance of accurately ascribing key PK metrics in the translational process to enable clinically meaningful predictions of efficacy.

Efficacy, safety, pharmacokinetic (PK), and pharmacodynamic (PD) support for talquetamab (tal) QW and Q2W dosing in patients (pts) with relapsed/refractory multiple myeloma (RRMM): Analyses from MonumenTAL-1.

8041 Background: Tal is a first-in-class bispecific antibody targeting the novel antigen G protein–coupled receptor family C group 5 member D. In MonumenTAL-1 (NCT03399799/NCT04634552), tal demonstrated an overall response rate (ORR) of > 70%, with clinically manageable safety in pts with RRMM. We summarize efficacy, safety, PD, and PK results supporting the recommended phase 2 doses (RP2Ds) of tal: 0.4 mg/kg QW and 0.8 mg/kg Q2W SC. Methods: In MonumenTAL-1, pts received IV (0.5–180 µg/kg) or SC (5–1600 µg/kg) tal doses in phase 1 and at the RP2Ds in phase 2, preceded by step-up doses. Blood samples were collected for the measurement of tal serum concentration and anti-tal antibodies (ADAs). Covariate effects on PK and efficacy were characterized. Efficacy and safety exposure–relationships (E–R) were evaluated using predicted PK metrics, ORR, and key treatment-emergent adverse events (TEAEs), including neurotoxicity, infections, cytopenia, ageusia, dysgeusia, weight loss, and cytokine release syndrome (CRS). Results: PK exposure was approximately dose proportional across IV and SC doses, with a SC bioavailability of 61.9%. At the RP2Ds, the mean concentration–time profiles were comparable and maintained at or above the concentration associated with the 90% maximal drug effect identified in an ex vivo cytotoxicity assay. The incidence of ADAs was 29% (QW) and 20% (Q2W). Presence of ADAs had no apparent impact on PK, safety (CRS, systemic administration–related reaction, or injection site reaction), or efficacy (ORR). At the RP2Ds, PD changes indicative of the proposed mechanism of action, such as T-cell activation, induction of cytokines, and T-cell redistribution, were comparable. Body weight, myeloma subtype, and International Staging System stage were identified as covariates for tal PK; however, there was no clinically relevant impact of age, sex, race, or mild/moderate hepatic or renal impairment on PK. Across SC doses in phase 1, ORR increased with exposure and reached a plateau at or above the RP2Ds, supporting the selected RP2Ds. Baseline myeloma subtype and extramedullary plasmacytoma status were identified as prognostic factors for ORR at the RP2Ds. There were no observed E–R for key TEAEs, including grade ≥3 infections or cytopenia, grade ≥2 weight loss, or grade 2 ageusia. No E–R for grade ≥1/≥2 CRS and peak tal exposure for any step-up doses, irrespective of tocilizumab administration, was observed. Dysgeusia rates were comparable between the 2 RP2Ds. Conclusions: Efficacy, safety, PK, and PD analyses support the selection of tal 0.4 mg/kg QW and 0.8 mg/kg Q2W SC regimens as RP2Ds. The RP2Ds provided comparable PK, PD, efficacy, and safety profiles. Covariates for PK exposure had no impact on E–R, and dose adjustment was not warranted. Clinical trial information: NCT03399799 , NCT04634552 .

Current Understanding of Inherited Modifiers of FVIII Pharmacokinetic Variation.

The inherited bleeding disorder hemophilia A involves the quantitative deficiency of the coagulation cofactor factor VIII (FVIII). Prophylactic treatment of severe hemophilia A patients with FVIII concentrates aims to reduce the frequency of spontaneous joint bleeding and requires personalized tailoring of dosing regimens to account for the substantial inter-individual variability of FVIII pharmacokinetics. The strong reproducibility of FVIII pharmacokinetic (PK) metrics between repeat analyses in the same individual suggests this trait is genetically regulated. While the influence of plasma von Willebrand factor antigen (VWF:Ag) levels, ABO blood group, and patient age on FVIII PK is well established, estimates suggest these factors account for less than 35% of the overall variability in FVIII PK. More recent studies have identified genetic determinants that modify FVIII clearance or half-life including VWF gene variants that impair VWF-FVIII binding resulting in the accelerated clearance of VWF-free FVIII. Additionally, variants in receptors that regulate the clearance of FVIII or the VWF-FVIII complex have been associated with FVIII PK. The characterization of genetic modifiers of FVIII PK will provide mechanistic insight into a subject of clinical significance and support the development of personalized treatment plans for patients with hemophilia A.

Alternative Pharmacokinetic Metrics in Single-Dose Studies to Ensure Bioequivalence of Prolonged-Release Products at Steady State—A Case Study

(1) Background: this article investigates which PK metrics in a single-dose study (concentration at the end of posology interval, Cτ, partial areas under the curve, pAUCs, or half-value duration, HVD) are more sensitive and less variable for predicting the failure of a prolonged-release product at steady-state that was the bioequivalent for Cmax, AUC0-t and AUC0-inf, in the single-dose study; (2) Methods: a cross-over study was performed in 36 subjects receiving desvenlafaxine 100 mg prolonged-release tablets. Conventional (Cmax, AUC0-t and AUC0-inf) and additional (Cτ, pAUCs and HVD) PK metrics were considered after single-dose conditions. Predicted PK metrics at steady state (AUC0-τ, Cmax,ss, and Cτ,ss) were derived using a population PK model approach; (3) Results: the existing differences in the shape of the concentration-time curves precluded to show equivalence for Cτ,ss in the simulated study at steady state. This failure to show equivalence at steady state was predicted by Cτ, pAUCs and HVD in the single-dose study. Cτ was the most sensitive metric for detecting the different shape, with a lower intra-subject variability than HVD; (4) Conclusions: conventional PK metrics for single-dose studies (Cmax, AUC0-t and AUC0-inf) are not enough to guarantee bioequivalence at steady state for prolonged-release products.

Machine Learning in Bioequivalence: Towards Identifying an Appropriate Measure of Absorption Rate

In this study, the modern tool of machine learning is used to address an old problem from a new perspective. Traditionally, the scientific basis for determining bioequivalence is based on a pharmacokinetic comparison, specifically the rate and extent of absorption between two products. Even though it is generally agreed that the peak plasma concentration (Cmax) should be used to measure the rate of absorption, several studies have raised concerns. Thus, alternative pharmacokinetic metrics have been proposed to address Cmax shortcomings. The aim of this study is to utilize unsupervised (principal component analysis) and supervised (random forest) machine learning algorithms to uncover the relationships among the pharmacokinetic parameters and identify the most suitable metric for absorption rate. One actual and three simulated donepezil bioequivalence datasets were utilized. For the needs of this study, a population pharmacokinetic model of donepezil was also developed and further used for the simulation of BE datasets with different absorption kinetics. Among the pharmacokinetic metrics explored, the newly proposed Cmax/Tmax ratio is also investigated. The latter was found to better reflect the absorption rate, regardless of the kinetic properties of absorption. This is one of the first studies utilizing machine learning in the field of bioequivalence.

A naïve pooled data approach for extrapolation of Phase 0 microdose trials to therapeutic dosing regimens.

Microdosing is a strategy to obtain knowledge of human pharmacokinetics prior to Phase I clinical trials. The most frequently used method to extrapolate microdose (≤100 μg) pharmacokinetics to therapeutic doses is based on linear extrapolation from a noncompartmental analysis (NCA) with a two-fold acceptance criterion between pharmacokinetic metrics of the extrapolated microdose and the therapeutic dose. The major disadvantage of NCA is the assumption of linear extrapolation of NCA metrics. In this study, we used a naïve pooled data (NPD) modeling approach to extrapolate microdose pharmacokinetics to therapeutic pharmacokinetics. Gemcitabine and anastrozole were used as examples of intravenous and oral drugs, respectively. Data from microdose studies were used to build a parent-metabolite model for gemcitabine and its metabolite 2',2'-difluorodeoxyuridine (dFdU) and a model for anastrozole. The pharmacokinetic microdose models were extrapolated to therapeutic doses. Extrapolation of the microdose showed differences in pharmacokinetic shape for gemcitabine and dFdU between the simulated and observed therapeutic concentrations, whereas the observed therapeutic concentrations for anastrozole were captured by the extrapolation. This study demonstrated the possible use and feasibility of an NPD modeling approach for the evaluation and application of microdose studies in early drug development. Last, physiologically-based pharmacokinetic modeling might be an alternative for microdose extrapolation of drugs with complex pharmacokinetics such as gemcitabine.

Pharmacokinetics and Bioequivalence of Sitagliptin Phosphate/Metformin Hydrochloride Tablets in Healthy Chinese Subjects: A Randomized, Open-Label, Crossover Study.

ObjectiveOur objective was to evaluate the pharmacokinetics and bioequivalence of test and reference (JANUMET®) formulations of sitagliptin phosphate/metformin hydrochloride tablets at a single dose of 50 mg/850 mg.MethodsThe study was a randomized, open-label, two-period, double-crossover trial. Volunteers under fasting (n = 24) and fed (n = 24) conditions were given a single oral dose of test or reference formulations of sitagliptin phosphate/metformin hydrochloride tablets 50 mg/850 mg. We used the liquid chromatography tandem mass spectrometry method to determine the concentrations of sitagliptin and metformin in the plasma of subjects. Pharmacokinetic metrics were calculated using the WinNonlin 7.0 program, and bioequivalence was evaluated using SAS 9.4.ResultsUnder the fasting condition, the 90% confidence intervals (CIs) of geometric mean ratio for maximum plasma drug concentration (Cmax), area under the plasma concentration–time curve from time zero to time t (AUC0–t), and AUC from time zero to infinity (AUC0–∞) of sitagliptin between the test and reference groups were 101.70–120.62%, 99.81–105.61%, and 100.27–106.12%, respectively; for metformin, they were 90.39–111.48%, 94.76–109.12%, and 95.76–110.38%, respectively. Under the fed condition, they were 102.12–117.31%, 100.80–107.81%, and 100.82–107.78%, respectively, for sitagliptin and 95.53–105.22%, 92.76–103.07%, and 93.40–104.14%, respectively, for metformin. Both were generally well-tolerated.ConclusionThe two formulations of sitagliptin phosphate/metformin hydrochloride tablets were bioequivalent under fasting and fed conditions in healthy Chinese subjects.

Association of partial systemic exposure and abuse potential for opioid analgesics with abuse deterrence labeling claims supporting product-specific guidance.

BackgroundOver the past decade, U.S. FDA has approved 10 opioid analgesics in abuse-deterrent formulations (ADFs). ADFs are intended to reduce abuse of a prescription opioid through manipulation of the product to use one or more routes of abuse. Although it is critically needed for evaluation of the abuse deterrent properties of an opioid product, the relationship between systemic exposure and likelihood of abuse of the opioid has not been fully characterized. To fill the current knowledge gap, we have evaluated the association of subjective measures predictive of abuse potential (e.g., scores of “drug liking,” “take drug again”), which are referred to as ‘pharmacodynamic (PD)’ responses for measuring abuse potential, with systemic exposure of the opioid using the data from all the clinical abuse potential trials submitted to FDA in support of the approval of innovator ADFs.MethodsExtensive pharmacokinetic (PK) and subjective response data from 11 clinical abuse potential trials in recreational opioid users following oral and nasal administration of intact and manipulated oxycodone, hydrocodone and morphine products from the FDA internal database were utilized for the present analysis. This retrospective study used data collected from January 11th, 2010 until March 25th, 2015. The potential relationship between PK metrics, especially those for early exposure measures, and the subjective measures of drug liking and take drug again as PD metrics of abuse potential were explored using linear and logistic regression analyses. Heterogeneity analysis was conducted to assess study-to-study variation and multi-level logistic regression analysis was used to affirm the identified PK-PD relationship based on pooled data.FindingsFollowing oral and nasal administration of intact and manipulated opioids, the maximum visual analogue scale (VAS) for Drug Liking was generally achieved no later than the time to peak plasma drug concentration. Both heterogeneity analysis and multi-level logistic regression indicated insignificant inter study variability for the evaluated PK-PD relationships. Duration of Drug Liking response (i.e., VAS ≥ 65) lasted for 2 to 4 h after drug administration. The early portion of the systemic area under the plasma concentration-time curve (AUC), e.g., partial AUCs in the first 3 h and 4 h were found to be associated with abuse potential measures including maximum Drug Liking VAS and maximum Taking Drug Again VAS. Neither a formulation factor (e.g., immediate-release vs. extended-release, intact vs. manipulated) nor a route of administration was identified as a significant factor together with early partial AUCs to predict the probability of maximum Drug Liking or maximum Take Drug Again responses being greater than or equal to 65.InterpretationOur assessment indicates that the measure of early systemic drug exposure of opioids is the best predictor of the abuse potential response in recreational opioid users following oral or nasal administration of a single dose of an intact or manipulated abuse deterrent opioids. Our findings support FDA's recommendation of comparative PK studies with early partial AUCs as a supportive PK metric for the assessment of abuse deterrent properties of generic opioid drug products in the general and product-specific guidance's of ADFs.FundingThe study was partially funded by Fiscal Year 2017 Critical Path of the Center for Drug Evaluation and Research at the U.S. Food and Drug Administration.

Pharmacokinetic Modeling and Predictive Performance: Practical Considerations for Therapeutic Monoclonal Antibodies

Population pharmacokinetic (PopPK) model parameter estimation and predictive performance depend on the data adequacy for model building. PopPK models of therapeutic monoclonal antibodies (mAbs) may not be well supported by commonly used sparse sampling in late-stage development because of the slow absorption (days) and long half-life (weeks)of mAbs, affecting accuracy of predicted exposure metrics which are often used to support drug development. A case study was presented for a representative mAb to compare the predictive performance of two established PopPK models from their respective data. Differences in datasets for model building (including sample size, sampling schedule and route of administration), model structure and parameters, and key derived exposure metrics were compared, and the resulting differences in model prediction were elaborated. With the majority of the data used for developing models being trough concentration (Ctrough) data, both models projected similar Ctrough and area under the concentration-time curve (AUC) but different peak concentrations (Cmax) at steady state following the same subcutaneous dose regimen. Our case study supports the importance of appropriate sampling schemes for PopPK model development and exposure metric estimation. We recommend collecting proper random pharmacokinetic samples, in addition to troughs, to allow adequate characterization of PopPK models for mAbs. Selecting the informative model and relevant pharmacokinetic metrics could be critical in driving drug development decision-making, especially in simulation-based exposure matching to inform doses in special populations such as pediatrics.

Assessment of carbapenems in a mouse model of Mycobacterium tuberculosis infection

We present further study of a subset of carbapenems, arising from a previously reported machine learning approach, with regard to their mouse pharmacokinetic profiling and subsequent study in a mouse model of sub-acute Mycobacterium tuberculosis infection. Pharmacokinetic metrics for such small molecules were compared to those for meropenem and biapenem, resulting in the selection of two carbapenems to be assessed for their ability to reduce M. tuberculosis bacterial loads in the lungs of infected mice. The original syntheses of these two carbapenems were optimized to provide multigram quantities of each compound. One of the two experimental carbapenems, JSF-2204, exhibited efficacy equivalent to that of meropenem, while both were inferior to rifampin. The lessons learned in this study point toward the need to further enhance the pharmacokinetic profiles of experimental carbapenems to positively impact in vivo efficacy performance.

Investigating the Impact of Gastric Emptying on Pharmacokinetic Parameters Using Delay Differential Equations and Principal Component Analysis.

Losartan presents multiple peaks after single oral administration, which can be attributed to gastric emptying. The aim of this study was to describe the multiple peak phenomenon of losartan using a delay differential model and a model with sine function. The impact of gastric emptying on pharmacokinetic parameters was investigated by applying principal component analysis to the individual parameter estimates. Using MonolixTM, two population pharmacokinetic models were developed to describe the multiple peak phenomenon; the first using delay differential equations and the second using a sine function. Matlab® delay differential equation solver was used to arithmetically solve both functions. Principal component analysis and all statistical analyses were performed in the R language. The description of losartan multiple peaks can be achieved by the use of either delay differential equations or typical sine wave functions. Principal component analysis unveiled the impact of gastric emptying on the pharmacokinetic parameters. In the case of the delay differential equation model, a negative relationship was found between the constant delay tau1 and the parameters reflecting rate and extent of absorption (i.e., area under the curve [AUC], peak plasma concentration [Cmax], and the absorption rate constant). Similar results were obtained from the sine model, where a higher amplitude and lower period (i.e., higher frequency) of gastric emptying were associated with higher AUC and Cmax values. The observed multiple peaks for certain drugs like losartan can be attributed to gastric emptying. Parameters describing gastric emptying can be associated with pharmacokinetic metrics like AUC and Cmax.