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Breast cancer is a complex and heterogeneous disease with varying cellular, genetic, epigenetic, and molecular expressions. The detection of intratumor heterogeneity in breast cancer poses significant challenges due to its complex multifaceted characteristics, yet its identification is crucial for guiding effective treatment decisions and understanding the disease progression. Currently, there exists no method capable of capturing the full extent of breast tumor heterogeneity. In this study, the aim is to identify and characterize metabolic heterogeneity in breast tumors using immune cells and an ultrafast laser-fabricated Immuno Nano Sensor. Combining spectral markers from both Natural Killer (NK) and T cells, a machine-learning approach is implemented to distinguish cancer from healthy samples, identify primary versus metastatic tumors, and determine estrogen receptor (ER)/progesterone receptor (PR) status at the single-cell level. The platform successfully distinguished heterogeneous breast cancer samples from healthy individuals, achieving 97.8% sensitivity and 92.2% specificity, and accurately identified primary tumors from metastatic tumors. Characteristic spectral signatures allow for discrimination between ER/PR-positive and negative tumors with 97.5% sensitivity. This study demonstrates the potential of immune cell-based metabolic profiling in providing a comprehensive assessment of breast tumor heterogeneity and paving the way for minimally invasive liquid biopsy approaches in breast cancer diagnosis and management., (© 2024 The Author(s). Small published by Wiley‐VCH GmbH.)

Aldehyde oxidase (AO) contributes to the clearance of many approved and investigational small molecule drugs, which are often dual substrates of AO and drug-metabolizing enzymes such as cytochrome P450s (CYPs). As such, the lack of established framework for quantitative translation of the clinical pharmacologic correlates of AO-mediated clearance represents an unmet need. This study aimed to evaluate the utility of physiologically based pharmacokinetic (PBPK) modeling in the development of AO and dual AO-CYP substrates. PBPK models were developed for capmatinib, idelalisib, lenvatinib, zaleplon, ziprasidone, and zoniporide, incorporating in vitro functional data from human liver subcellular fractions and human hepatocytes. Prediction of metabolic elimination with/without the additional empirical scaling factors (ESFs) was assessed. Clinical pharmacokinetics, human mass balance, and drug-drug interaction (DDI) studies with CYP3A4 modulators, where available, were used to refine/verify the models. Due to the lack of clinically significant AO-DDIs with known AO inhibitors, the fraction metabolized by AO (fm AO ) was verified indirectly. Clearance predictions were improved by using ESFs (GMFE ≤1.4-fold versus up to fivefold with physiologically-based scaling only). Observed fm i from mass balance studies were crucial for model verification/refinement, as illustrated by capmatinib, where the fm AO (40%) was otherwise underpredicted up to fourfold. Subsequently, independent DDI studies with ketoconazole, itraconazole, rifampicin, and carbamazepine verified the fm CYP3A4 , with predicted ratios of the area under the concentration-time curve (AUCR) within 1.5-fold of the observations. In conclusion, this study provides a novel PBPK-based framework for predicting AO-mediated pharmacokinetics and quantitative assessment of clinical DDI risks for dual AO-CYP substrates within a totality-of-evidence approach., (© 2024 The Author(s). CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

While some model-informed drug development frameworks are well recognized as enabling clinical trials, the value of disease progression modeling (DPM) in impacting medical product development has yet to be fully realized. The Clinical Trials Transformation Initiative assembled a diverse project team from across the patient, academic, regulatory, and industry sectors of practice to advance the use of DPM for decision making in clinical trials and medical product development. This team conducted a scoping review to explore current applications of DPM and convened a multi-stakeholder expert meeting to discuss its value in medical product development. In this article, we present the scoping review and expert meeting output and propose key questions that medical product developers and regulators may use to inform clinical development strategy, appreciate the therapeutic context and endpoint selection, and optimize trial design with disease progression models. By expanding awareness of the unique value of DPM, this article does not aim to be technical in nature but rather aims to highlight the potential of DPM to improve the quality and efficiency of medical product development., (© 2024 The Author(s). Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

The Project Optimus initiative from the FDA introduced a new dose optimization and selection paradigm in oncology drug development. The FDA has outlined approaches to dose optimization for single agents, but multiple oncology drugs are being developed for use in combination with other therapies. Dose optimization in the context of combination drug development is complex and requires a case-by-case approach. It necessitates commitment to the totality of available evidence, leveraging all relevant data on mechanism of action, nonclinical and clinical pharmacology, safety, and principles of model-informed drug development. In this article, we outline key considerations for sponsors and investigators pursuing dose optimization with combinatorial regimens. We illustrate important strategies for dose optimization in the combination setting using a range of hypothetical case examples that represent typical drug development scenarios. Close discussions and collaboration with regulators regarding the optimal approaches to these scenarios will continue to be critical.

With the International Conference on Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) E17 guidelines in effect from 2018, the design of Asia-inclusive multiregional clinical trials (MRCTs) has been streamlined, thereby enabling efficient simultaneous global development. Furthermore, with the recent regulatory reforms in China and its drug administration joining the ICH as a full regulatory member, early participation of China in the global clinical development of novel investigational drugs is now feasible. This would also allow for inclusion of the region in the geographic footprint of pivotal MRCTs leveraging principles of the ICH E5 and E17. Herein, we describe recent case examples of model-informed Asia-inclusive global clinical development in the EMD Serono portfolio, as applied to the ataxia telangiectasia and Rad3-related inhibitors, tuvusertib and berzosertib (oncology), the toll-like receptor 7/8 antagonist, enpatoran (autoimmune diseases), the mesenchymal-epithelial transition factor inhibitor tepotinib (oncology), and the antimetabolite cladribine (neuroimmunological disease). Through these case studies, we illustrate pragmatic approaches to ethnic sensitivity assessments and the application of a model-informed drug development toolkit including population pharmacokinetic/pharmacodynamic modeling and pharmacometric disease progression modeling and simulation to enable early conduct of Asia-inclusive MRCTs. These examples demonstrate the value of a Totality of Evidence approach where every patient's data matter for de-risking ethnic sensitivity to inter-population variations in drug- and disease-related intrinsic and extrinsic factors, enabling inclusive global development strategies and timely evidence generation for characterizing benefit/risk of the proposed dosage in Asian populations., (© 2024 The Author(s). Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Cetuximab was initially developed and approved as a first-line treatment in patients with unresectable metastatic colorectal cancer (mCRC) for weekly administration (250 mg/m 2 Q1W with 400 mg/m 2 loading dose). An every-2-weeks schedule (500 mg/m 2 Q2W) was approved recently by several health authorities. Being synchronized with chemotherapy, Q2W administration should improve patients' convenience and healthcare resource utilization. Herein, we present evidence of non-inferiority of Q2W cetuximab, compared with Q1W dosing using pharmacometrics modeling and clinical trial simulation (CTS). Pooled data from five phase I-III clinical trials in 852 patients with KRAS wild-type mCRC treated with Q1W or Q2W cetuximab were modeled using a population exposure-tumor size (TS) model linked to overall survival (OS); exposure was derived from a previously established population pharmacokinetic model. A semi-mechanistic TS model adapted from the Claret model incorporated killing rate proportional to cetuximab area under the concentration-time curve over 2 weeks (AUC) with Eastern Cooperative Oncology Group (ECOG) status as covariate on baseline TS. The OS was modeled with Weibull hazard using ECOG, baseline TS, primary tumor location, and predicted percent change in TS at 8 weeks as covariates. Model-based simulations revealed indistinguishable early tumor shrinkage and survival between Q2W vs. Q1W cetuximab. CTS evaluated OS non-inferiority (predefined margin of 1.25) in 1,000 trials, each with 2,000 virtual patients receiving Q2W or Q1W cetuximab (1:1), and demonstrated non-inferiority in 94% of cases. Taken together, these analyses provide model-based evidence for clinical non-inferiority of Q2W vs. Q1W cetuximab in mCRC with potential benefits to patients and healthcare providers., (© 2024 Emd Serono Research & Development Institute, Inc. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Tepotinib is approved for the treatment of patients with non-small-cell lung cancer harboring MET exon 14 skipping alterations. While edema is the most prevalent adverse event (AE) and a known class effect of MET inhibitors including tepotinib, there is still limited understanding about the factors contributing to its occurrence. Herein, we apply machine learning (ML)-based approaches to predict the likelihood of occurrence of edema in patients undergoing tepotinib treatment, and to identify factors influencing its development over time. Data from 612 patients receiving tepotinib in five Phase I/II studies were modeled with two ML algorithms, Random Forest, and Gradient Boosting Trees, to predict edema AE incidence and severity. Probability calibration was applied to give a realistic estimation of the likelihood of edema AE. Best model was tested on follow-up data and on data from clinical studies unused while training. Results showed high performances across all the tested settings, with F1 scores up to 0.961 when retraining the model with the most relevant covariates. The use of ML explainability methods identified serum albumin as the most informative longitudinal covariate, and higher age as associated with higher probabilities of more severe edema. The developed methodological framework enables the use of ML algorithms for analyzing clinical safety data and exploiting longitudinal information through various covariate engineering approaches. Probability calibration ensures the accurate estimation of the likelihood of the AE occurrence, while explainability tools can identify factors contributing to model predictions, hence supporting population and individual patient-level interpretation., (© 2024 The Author(s). Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

The landscape of oncology drug development has witnessed remarkable advancements over the last few decades, significantly improving clinical outcomes and quality of life for patients with cancer. Project Optimus, introduced by the U.S. Food and Drug Administration, stands as a groundbreaking endeavor to reform dose selection of oncology drugs, presenting both opportunities and challenges for the field. To address complex dose optimization challenges, an Oncology Dose Optimization IQ Working Group was created to characterize current practices, provide recommendations for improvement, develop a clinical toolkit, and engage Health Authorities. Historically, dose selection for cytotoxic chemotherapeutics has focused on the maximum tolerated dose, a paradigm that is less relevant for targeted therapies and new treatment modalities. A survey conducted by this group gathered insights from member companies regarding industry practices in oncology dose optimization. Given oncology drug development is a complex effort with multidimensional optimization and high failure rates due to lack of clinically relevant efficacy, this Working Group advocates for a case-by-case approach to inform the timing, specific quantitative targets, and strategies for dose optimization, depending on factors such as disease characteristics, patient population, mechanism of action, including associated resistance mechanisms, and therapeutic index. This white paper highlights the evolving nature of oncology dose optimization, the impact of Project Optimus, and the need for a tailored and evidence-based approach to optimize oncology drug dosing regimens effectively., (© 2024 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Recent advances in the use of stable isotopes necessitate novel synthesis techniques for isotope separation and enrichment that are scalable and offer high throughput. Stable-isotope-enriched nanostructures can offer unique advantages as nanomedicines, safe tracers, and labels and are critical for applications in various industrial processes, metabolic research, and medicine. So far, there exists no method to synthesize miniature isotope-enriched materials at the nanoscale. In this study, an ultrafast Laser-induced isotope enrichment at nanoscale (LIIEN) is put forward to synthesize isotope-enriched nanostructures, eliminating the need for large equipment and expenses, thereby demonstrating a lab-scale isotope enrichment process. A significant isotope enrichment for Carbon nanostructures is observed. The isotope enrichment can be attributed to the redistribution of isotope ions in the plasma plume explained by the plasma centrifuge model. The LIIEN synthesized structures exhibit excellent Surface-Enhanced Raman Scattering (SERS) signal enhancement and reproducibility, making them potential candidates for SERS-based biomolecule sensing. This technique is an efficient method to fabricate nanosized isotope-enriched structures of characteristic properties by carefully tuning laser parameters at ambient conditions., (© 2024 Wiley‐VCH GmbH.)

Importance: After a hypertensive disorder of pregnancy, hypertension can worsen in the postpartum period following hospital discharge. Risk factors for ongoing hypertension and associated outcomes have not been well characterized., Objective: To identify risk factors and characterize outcomes for individuals with ongoing hypertension and severe hypertension following hospital discharge post partum through a hospital system's remote blood pressure (BP) management program., Design, Setting, and Participants: This cohort study involved a population-based sample of individuals with a new-onset hypertensive disorder of pregnancy (preeclampsia or gestational hypertension) and no prepregnancy hypertension who delivered between September 2019 and June 2021. Participants were enrolled in a remote BP monitoring and management program at a postpartum unit at a referral hospital. Data analysis was performed from August 2021 to January 2023., Exposure: Inpatient postpartum BP categories., Main Outcomes and Measures: The primary outcomes were readmission and emergency department visits within the first 6 weeks post partum. Logistic regression was used to model adjusted odds ratios (aORs) and 95% CIs., Results: Of 2705 individuals in the cohort (mean [SD] age, 29.8 [5.7] years), 2214 (81.8%) had persistent hypertension post partum after hospital discharge, 382 (14.1%) developed severe hypertension after discharge, and 610 (22.6%) had antihypertensive medication initiated after discharge. Individuals with severe hypertension had increased odds of postpartum emergency department visits (aOR, 1.85; 95% CI, 1.17-2.92) and hospital readmissions (aOR, 6.75; 95% CI, 3.43-13.29) compared with individuals with BP normalization. When inpatient postpartum BP categories were compared with outpatient home BP trajectories to inform optimal thresholds for inpatient antihypertensive medication initiation, there was significant overlap between postdischarge BP trajectories among those with inpatient systolic BP greater than or equal to 140 to 149 mm Hg and/or diastolic BP greater than or equal to 90 to 99 mm Hg and those with systolic BP greater than or equal to 150 mm Hg and/or diastolic BP greater than or equal to 100 mm Hg., Conclusions and Relevance: This cohort study found that more than 80% of individuals with hypertensive disorders of pregnancy had ongoing hypertension after hospital discharge, with approximately 14% developing severe hypertension. These data support the critical role of remote BP monitoring programs and highlight the need for improved tools for risk stratification and consideration of liberalization of thresholds for medication initiation post partum.

Mobocertinib (TAK-788) is a first-in-class oral epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor that received accelerated approval for the treatment of patients with non-small cell lung cancer with EGFR exon 20 insertion mutations previously treated with platinum-based chemotherapy. This phase 1, 2-period, study was conducted to assess the absolute bioavailability of mobocertinib (Period 1), as well as mass balance, pharmacokinetics, metabolism, and excretion of [ 14 C]-mobocertinib (Period 2) in healthy adult males. In Period 1, participants received a single oral capsule dose of 160 mg mobocertinib, followed by a 15-minute intravenous infusion of 50 µg (~ 2 µCi) [ 14 C]-mobocertinib administered from 3.75 to 4 h after the capsule dose. In Period 2, a single oral dose of 160 mg (~ 100 µCi) [ 14 C]-mobocertinib was administered as an oral solution. The geometric mean absolute bioavailability of mobocertinib was determined to be 36.7%. After oral administration of [ 14 C]-mobocertinib, mobocertinib and its active metabolites, AP32960 and AP32914, were minor components in plasma, accounting for only 0.275% of total plasma radioactivity as the majority of mobocertinib-related material was covalently bound to plasma proteins. The geometric mean percentage of the administered radioactive dose recovered in the urine and feces was 3.57% and 76.0%, respectively. Only 0.39% of the oral dose of [ 14 C]-mobocertinib was recovered in the urine as mobocertinib; thus, indicating that renal excretion of unchanged drug was a very minor pathway of elimination. In both treatment periods, mobocertinib was generally safe and well-tolerated as all adverse events were Grade 1 in severity. (Trial registration number ClinicalTrials.gov NCT03811834. Registration date January 22, 2019)., (© 2024. The Author(s).)

Background: The aim of this study was to evaluate the association between early postpartum weight change and (1) hospital readmission and (2) 2-week blood pressure trajectory., Methods and Results: This retrospective study cohort included 1365 individuals with a hypertensive disorder of pregnancy enrolled in a postpartum hypertension remote monitoring program. Exposure was percentage weight change from delivery to first weight recorded within 10 days postpartum. We first modeled likelihood of hospital readmission within 8 weeks postpartum using logistic regression adjusting for age, race, insurance, type of hypertensive disorder of pregnancy, early body mass index, gestational weight gain, mode of delivery, and any discharge antihypertensive medications. We then performed case-control analysis additionally matching in a 1:3 ratio on breastfeeding, early body mass index, discharge on antihypertensive medications, and days between weight measurements. Both analytic approaches were repeated, limiting to readmissions attributable to hypertension or heart failure. Finally, we compared blood pressure trajectories over first 2 weeks postpartum. Individuals who did not lose weight in the early postpartum period had more admissions compared with weight loss groups (group 3: 14.1% versus group 2: 5.8% versus group 1: 4.5%). These individuals had 4 times the odds of postpartum readmissions (adjusted odds ratio [aOR], 3.9 [95% CI, 1.8-8.6]) to 7 (aOR, 7.8 [95% CI, 2.3-26.5]) compared with those with the most weight loss. This association strengthened when limited to hypertension or heart failure readmissions. These individuals also had more adverse postpartum blood pressure trajectories, with significant differences by weight change group., Conclusions: Weight change is readily accessible and may identify individuals at high risk for postpartum readmission following a hypertensive disorder of pregnancy who could benefit from targeted interventions.

Biofouling is the most common cause of bacterial contamination in implanted materials/devices resulting in severe inflammation, implant mobilization, and eventual failure. Since bacterial attachment represents the initial step toward biofouling, developing synthetic surfaces that prevent bacterial adhesion is of keen interest in biomaterials research. In this study, we develop antifouling nanoplatforms that effectively impede bacterial adhesion and the consequent biofilm formation. We synthesize the antifouling nanoplatform by introducing silicon (Si)/silica nanoassemblies to the surface through ultrafast ionization of Si substrates. We assess the effectiveness of these nanoplatforms in inhibiting Escherichia coli ( E. coli ) adhesion. The findings reveal a significant reduction in bacterial attachment on the nanoplatform compared to untreated silicon, with bacteria forming smaller colonies. By manipulating physicochemical characteristics such as nanoassembly size/concentration and nanovoid size, we further control bacterial attachment. These findings suggest the potential of our synthesized nanoplatform in developing biomedical implants/devices with improved antifouling properties., (© 2024 IOP Publishing Ltd.)

Enpatoran is a novel, highly selective, and potent dual toll-like receptor (TLR)7 and TLR8 inhibitor currently under development for the treatment of autoimmune disorders including systemic lupus erythematosus (SLE), cutaneous lupus erythematosus (CLE), and myositis. The ongoing phase II study (WILLOW; NCT05162586) is evaluating enpatoran for 24 weeks in patients with active SLE or CLE and is currently recruiting. To support development of WILLOW as an Asia-inclusive multiregional clinical trial (MRCT) according to International Conference on Harmonisation E5 and E17 principles, we have evaluated ethnic sensitivity to enpatoran based on clinical pharmacokinetic (PK), pharmacodynamic (PD), and safety data from an ethno-bridging study (NCT04880213), supplemented by relevant quantitative PK, PD, and disease trajectory modeling (DTM) results, and drug metabolism/disease knowledge. A single-center, open-label, sequential dose group study in White and Japanese subjects matched by body weight, height, and sex demonstrated comparable PK and PD properties for enpatoran in Asian vs. non-Asian (White and other) subjects across single 100, 200, and 300 mg orally administered doses. DTM suggested no significant differences in SLE disease trajectory for Asian vs. non-Asian individuals. Aldehyde oxidase (AOX) is considered to be a key contributor to enpatoran metabolism, and a literature review indicated no relevant ethnic differences in AOX function based on in vitro and clinical PK data from marketed drugs metabolized by AOX, supporting the conclusion of low ethnic sensitivity for enpatoran. Taken together, the inclusion of Asian patients in MRCTs including WILLOW was informed based on a Totality of Evidence approach., (© 2024 The Healthcare Business of Merck KGaA. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Project Optimus is a US Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. Although there is much promise in this initiative, there are several challenges that need to be addressed, including multidimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long-term tolerability beyond dose-limiting toxicities, and the lack of reliable biomarkers for long-term efficacy. Through the lens of Totality of Evidence and with the mindset of model-informed drug development, we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity., (© 2023 Emd Serono Research and Development Institute, Inc. Pfizer Inc and The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

The increasing breadth and depth of resolution in biological and clinical data, including -omics and real-world data, requires advanced analytical techniques like artificial intelligence (AI) and machine learning (ML) to fully appreciate the impact of multi-dimensional population variability in intrinsic and extrinsic factors on disease progression and treatment outcomes. Integration of advanced data analytics in Quantitative Pharmacology is crucial for drug-disease knowledge management, enabling precise, efficient and inclusive drug development and utilization - an application we refer to as model-informed precision medicine. AI/ML enables characterization of the molecular and clinical sources of heterogeneity in disease trajectory, advancing end point qualification and biomarker discovery, and informing patient enrichment for proof-of-concept studies as well as trial designs for efficient evidence generation incorporating digital twins and virtual control arms. Explainable ML methods are valuable in elucidating predictors of efficacy and safety of pharmacological treatments, thereby informing response monitoring and risk mitigation strategies. In oncology, emerging opportunities exist for development of the next generation of disease models via ML-assisted joint longitudinal modeling of high-dimensional biomarker data such as circulating tumor DNA and radiomics profiles as predictors of survival outcomes. Finally, mining real-world data leveraging ML algorithms enables understanding of the impact of exclusion criteria on clinical outcomes, thereby informing rational design of appropriately inclusive clinical trials through data-driven broadening of eligibility criteria. Herein, we provide an overview of the aforementioned contexts of use of ML in drug-disease modeling based on examples across multiple therapeutic areas including neurology, rare diseases, autoimmune diseases, oncology and immuno-oncology., (© 2023 Merck KGaA Darmstadt. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Brigatinib is an oral anaplastic lymphoma kinase (ALK) inhibitor approved for the treatment of ALK-positive metastatic non-small cell lung cancer. In vitro studies indicated that brigatinib is primarily metabolized by CYP2C8 and CYP3A4 and inhibits P-gp, BCRP, OCT1, MATE1, and MATE2K. Clinical drug-drug interaction (DDI) studies with the strong CYP3A inhibitor itraconazole or the strong CYP3A inducer rifampin demonstrated that CYP3A-mediated metabolism was the primary contributor to overall brigatinib clearance in humans. A physiologically-based pharmacokinetic (PBPK) model for brigatinib was developed to predict potential DDIs, including the effect of moderate CYP3A inhibitors or inducers on brigatinib pharmacokinetics (PK) and the effect of brigatinib on the PK of transporter substrates. The developed model was able to predict clinical DDIs with itraconazole (area under the plasma concentration-time curve from time 0 to infinity [AUC ∞ ] ratio [with/without itraconazole]: predicted 1.86; observed 2.01) and rifampin (AUC ∞ ratio [with/without rifampin]: predicted 0.16; observed 0.20). Simulations using the developed model predicted that moderate CYP3A inhibitors (e.g., verapamil and diltiazem) may increase brigatinib AUC ∞ by ~40%, whereas moderate CYP3A inducers (e.g., efavirenz) may decrease brigatinib AUC ∞ by ~50%. Simulations of potential transporter-mediated DDIs predicted that brigatinib may increase systemic exposures (AUC ∞ ) of P-gp substrates (e.g., digoxin and dabigatran) by 15%-43% and MATE1 substrates (e.g., metformin) by up to 29%; however, negligible effects were predicted on BCRP-mediated efflux and OCT1-mediated uptake. The PBPK analysis results informed dosing recommendations for patients receiving moderate CYP3A inhibitors (40% brigatinib dose reduction) or inducers (up to 100% increase in brigatinib dose) during treatment, as reflected in the brigatinib prescribing information., (© 2024 Takeda Pharmaceuticals. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Like other monoclonal antibodies, immune checkpoint inhibitors may be immunogenic in some patients, potentially affecting pharmacokinetics (PKs) and clinical outcomes. In post hoc analyses, we characterized antidrug antibody (ADA) development with avelumab monotherapy in patients with metastatic Merkel cell carcinoma (mMCC) from the JAVELIN Merkel 200 trial (first-line [1L; N = 116] and second-line or later [≥2L; N = 88] cohorts) or with advanced urothelial carcinoma (aUC) from the JAVELIN Bladder 100 (1L maintenance [N = 350]) and JAVELIN Solid Tumor (≥2L [N = 249]) trials. Treatment-emergent ADAs developed in a numerically higher proportion of patients with aUC (1L maintenance, 19.1%; ≥2L, 18.1%) versus mMCC (1L, 8.2%; ≥2L, 8.9%); incidences within tumor types were similar by line of therapy. In PK analyses, numerically lower avelumab trough concentration and higher baseline clearance were observed in treatment-emergent ADA+ versus ADA- subgroups; however, differences were not clinically relevant. Numerical differences in overall survival, progression-free survival, or objective response rate by ADA status were observed; however, no clinically meaningful trends were identified. Proportions of patients with treatment-emergent adverse events (TEAEs; any grade or grade 3/4), serious TEAEs, TEAEs leading to treatment discontinuation, or infusion-related reactions were similar, with overlapping 80% confidence intervals between ADA subgroups. Efficacy and safety observations were similar in subgroups defined by early development of ADA+ status during treatment. In conclusion, no meaningful differences in PKs, efficacy, and safety were observed between subgroups of avelumab-treated patients with different ADA status. Overall, these data suggest that ADAs are not relevant for treatment decisions with avelumab., (© 2024 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Several investigational agents are under evaluation in systemic lupus erythematosus (SLE) clinical trials but quantitative frameworks to enable comparison of their efficacy to reference benchmark treatments are lacking. To benchmark SLE treatment effects and identify clinically important covariates, we developed a model-based meta-analysis (MBMA) within a latent variable model framework for efficacy end points and SLE composite end point scores (BILAG-based Composite Lupus Assessment and Systemic Lupus Erythematosus Responder Index) using aggregate-level data on approved and investigational therapeutics. SLE trials were searched using PubMed and www.clinicaltrials.gov for treatment name, SLE and clinical trial as search criteria that resulted in four data structures: (1) study and investigational agent, (2) dose and regimen, (3) baseline descriptors, and (4) outcomes. The final dataset consisted of 25 studies and 81 treatment arms evaluating 16 different agents. A previously developed (K Goteti et al. 2022) SLE latent variable model of data from placebo arms (placebo + standard of care treatments) was used to describe aggregate SLE end points over time for the various SLE placebo and treatment arms in a Bayesian MBMA framework. Continuous dose-effect relationships using a maximum effect model were included for anifrolumab, belimumab, CC-220 (iberdomide), epratuzumab, lulizumab pegol, and sifalimumab, whereas the remaining treatments were modeled as discrete dose effects. The final MBMA model was then used to benchmark these compounds with respect to the maximal efficacy on the latent variable compared to the placebo. This MBMA illustrates the application of latent variable models in understanding the trajectories of composite end points in chronic diseases and should enable model-informed development of new investigational agents in SLE., (© 2023 EMD Serono Inc. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

This analysis aimed to quantify tumor dynamics in patients receiving either bintrafusp alfa (BA) or pembrolizumab, by population pharmacokinetic (PK)-pharmacodynamic modeling, and investigate clinical and molecular covariates describing the variability in tumor dynamics by pharmacometric and machine-learning (ML) approaches. Data originated from two clinical trials in patients with biliary tract cancer (BTC; NCT03833661) receiving BA and non-small cell lung cancer (NSCLC; NCT03631706) receiving BA or pembrolizumab. Individual drug exposure was estimated from previously developed population PK models. Population tumor dynamics models were developed for each drug-indication combination, and covariate evaluations performed using nonlinear mixed-effects modeling (NLME) and ML (elastic net and random forest models) approaches. The three tumor dynamics' model structures all included linear tumor growth components and exponential tumor shrinkage. The final BTC model included the effect of drug exposure (area under the curve) and several covariates (demographics, disease-related, and genetic mutations). Drug exposure was not significant in either of the NSCLC models, which included two, disease-related, covariates in the BA arm, and none in the pembrolizumab arm. The covariates identified by univariable NLME and ML highly overlapped in BTC but showed less agreement in NSCLC analyses. Hyperprogression could be identified by higher tumor growth and lower tumor kill rates and could not be related to BA exposure. Tumor size over time was quantitatively characterized in two tumor types and under two treatments. Factors potentially related to tumor dynamics were assessed using NLME and ML approaches; however, their net impact on tumor size was considered as not clinically relevant., (© 2023 Merck KGaA and The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Metastatic lung cancers are considered one of the most clinically significant malignancies, comprising about 40% of deaths caused by cancers. Detection of lung cancer metastasis prior to symptomatic relapse is critical for timely diagnosis and clinical management. The onset of cancer metastasis is indicated by the manifestation of tumor-shed signatures from the primary tumor in peripheral circulation. A subset of this population, characterized as the metastasis-initiating stem cells, are capable of invasion, tumor initiation, and propagation of metastasis at distant sites. In this study, we have developed a SERS-functionalised L-MISC (Lung-Metastasis Initiating Stem Cells) nanosensor to accurately capture the trace levels of metastatic signatures directly from patient blood. We investigated the signatures of cancer stem cell enriched heterogenous population of primary and metastatic lung cancer cells to establish a metastatic profile unique to lung cancer. Multivariate statistical analyses revealed statistically significant differences in the molecular profiles of healthy, primary, and metastatic cell populations. The single-cell sensitivity of L-MISC nanosensor enabled a label-free detection of MISCs with high sensitivity and specificity. By employing a robust machine learning model, our diagnostic methodology can accurately detect metastatic lung cancer from not more than 5 μl of blood. A pilot validation of our study was carried out using clinical samples for the prediction of metastatic lung cancers resulting in 100% diagnostic sensitivity. The L-MISC nanosensor is a potential tool for highly rapid, non-invasive, and accurate diagnosis of lung cancer metastasis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Purpose: The effectiveness of a dexamethasone (DEX)-free regimen for chemotherapy-induced nausea and vomiting (CINV) prophylaxis in patients receiving highly emetogenic chemotherapy (HEC) is not known., Methods: This was a double-blind, phase III trial designed to show the noninferiority of a DEX-free regimen (olanzapine, palonosetron, and fosaprepitant [OPF]) compared with the DEX-containing regimen (olanzapine, palonosetron, and DEX [OPD]). Chemotherapy-naïve patients age 18-80 years receiving single-day HEC were randomly assigned 1:1 to receive either the OPD regimen or the OPF regimen. The primary objective was to compare complete response (CR) rates for vomiting during the overall period (start of chemotherapy to 120 hours). Secondary objectives included CR for vomiting during the acute period (0-24 hours) and delayed period (24-120 hours), CR for nausea, and comparison of toxicities and patient-reported outcomes., Results: Three hundred forty-six patients received the study interventions, 174 in the OPD arm and 172 in the OPF arm. The DEX-free OPF arm had significantly higher CR rates for vomiting compared with the DEX-containing OPD arm in acute (94.7% v 85.6%; P < .004), delayed (81.9% v 50.5%; P < .001), and overall (79.6% v 48.8%; P < .001) periods. For nausea, CR rates in the OPF arm were higher in delayed (53.4% v 39.6%; P = .009) and overall (50.5% v 39.1%; P = .031) periods but not in the acute period (77.9% v 81.6%; P = .39). Fatigue ( P = .009) and drowsiness ( P = .002) were more in the OPF arm in the acute period and insomnia ( P < .001) in the OPD arm in the overall period., Conclusion: This study shows that a DEX-free OPF regimen is efficacious and should be considered a standard option for acute and delayed CINV prophylaxis for HEC.

Xevinapant, an oral inhibitor of apoptosis protein (IAP) inhibitor, demonstrated efficacy in combination with chemoradiotherapy in a randomized phase II study (NCT02022098) in patients with locally advanced squamous cell carcinoma of the head and neck at 200 mg/day on days 1-14 of a 3-week cycle. To confirm 200 mg/day as the recommended phase III dose (RP3D), we integrated preclinical, clinical, pharmacokinetic/pharmacodynamic (PK/PD), and exposure-response modeling results. Population PK/PD modeling of IAP inhibition in peripheral blood mononuclear cells in 21 patients suggested the pharmacologically active dose range was 100-200 mg/day, with a trend for more robust inhibition at the end of the dosing interval at 200 mg/day based on an indirect response model. Additionally, the unbound average plasma concentration at 200 mg/day was similar to that associated with efficacy in preclinical xenograft models. Logistic regression exposure-response analyses of data from 62 patients in the phase II study showed exposure-related increases in probabilities of locoregional control at 18 months (primary end point), overall response, complete response, and the radiosensitization mechanism-related composite safety end point "mucositis and/or dysphagia" (P < 0.05). Exposure-response relationships were not discernible for 12 of 13 evaluated safety end points, incidence of dose reductions, and time to first dose reduction. Quantitative integration of all available data, including model-derived target inhibition profiles, positive exposure-efficacy relationships, and lack of discernible exposure-safety relationships for most safety end points, supports selection of xevinapant 200 mg/day on days 1-14 of a 3-week cycle as the RP3D, allowing for successive dose reductions to 150 and 100 mg/day to manage adverse events., (© 2023 EMD Serono and The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Underestimation of aldehyde oxidase (AO)-mediated clearance by current in vitro assays leads to uncertainty in human dose projections, thereby reducing the likelihood of success in drug development. In the present study we first evaluated the current drug development practices for AO substrates. Next, the overall predictive performance of in vitro-in vivo extrapolation of unbound hepatic intrinsic clearance (CL int,u ) and unbound hepatic intrinsic clearance by AO (CL int,u,AO ) was assessed using a comprehensive literature database of in vitro (human cytosol/S9/hepatocytes) and in vivo (intravenous/oral) data collated for 22 AO substrates (total of 100 datapoints from multiple studies). Correction for unbound fraction in the incubation was done by experimental data or in silico predictions. The fraction metabolized by AO (fm AO ) determined via in vitro/in vivo approaches was found to be highly variable. The geometric mean fold errors (gmfe) for scaled CL int,u (mL/min/kg) were 10.4 for human hepatocytes, 5.6 for human liver cytosols, and 5.0 for human liver S9, respectively. Application of these gmfe's as empirical scaling factors improved predictions (45%-57% within twofold of observed) compared with no correction (11%-27% within twofold), with the scaling factors qualified by leave-one-out cross-validation. A road map for quantitative translation was then proposed following a critical evaluation on the in vitro and clinical methodology to estimate in vivo fm AO In conclusion, the study provides the most robust system-specific empirical scaling factors to date as a pragmatic approach for the prediction of in vivo CL int,u,AO in the early stages of drug development. SIGNIFICANCE STATEMENT: Confidence remains low when predicting in vivo clearance of AO substrates using in vitro systems, leading to de-prioritization of AO substrates from the drug development pipeline to mitigate risk of unexpected and costly in vivo impact. The current study establishes a set of empirical scaling factors as a pragmatic tool to improve predictability of in vivo AO clearance. Developing clinical pharmacology strategies for AO substrates by utilizing mass balance/clinical drug-drug interaction data will help build confidence in fm AO ., (Copyright © 2023 by The Author(s).)

The dose/exposure-efficacy analyses are often conducted separately for oncology end points like best overall response, progression-free survival (PFS) and overall survival (OS). Multistate models offer to bridge these dose-end point relationships by describing transitions and transition times from enrollment to response, progression, and death, and evaluating transition-specific dose effects. This study aims to apply the multistate pharmacometric modeling and simulation framework in a dose optimization setting of bintrafusp alfa, a fusion protein targeting TGF-β and PD-L1. A multistate model with six states (stable disease [SD], response, progression, unknown, dropout, and death) was developed to describe the totality of endpoints data (time to response, PFS, and OS) of 80 patients with non-small cell lung cancer receiving 500 or 1200 mg of bintrafusp alfa. Besides dose, evaluated predictor of transitions include time, demographics, premedication, disease factors, individual clearance derived from a pharmacokinetic model, and tumor dynamic metrics observed or derived from tumor size model. We found that probabilities of progression and death upon progression decreased over time since enrollment. Patients with metastasis at baseline had a higher probability to progress than patients without metastasis had. Despite dose failed to be statistically significant for any individual transition, the combined effect quantified through a model with dose-specific transition estimates was still informative. Simulations predicted a 69.2% probability of at least 1 month longer, and, 55.6% probability of at least 2-months longer median OS from the 1200 mg compared to the 500 mg dose, supporting the selection of 1200 mg for future studies., (© 2023 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Glioblastoma (GBM), the most aggressive and lethal brain cancer, is detected only in the advanced stage, resulting in a median survival rate of 15 months. Therefore, there is an urgent need to establish GBM diagnosis tools to identify the tumor accurately. The clinical relevance of the current liquid biopsy techniques for GBM diagnosis remains mostly undetermined, owing to the challenges posed by the blood-brain barrier (BBB) that restricts biomarkers entering the circulation, resulting in the unavailability of clinically validated circulating GBM markers. GBM-specific liquid biopsy for diagnosis and prognosis of GBM has not yet been developed. Here, we introduce extracellular vesicles of GBM cancer stem cells (GBM CSC-EVs) as a previously unattempted, stand-alone GBM diagnosis modality. As GBM CSCs are fundamental building blocks of tumor initiation and recurrence, it is desirable to investigate these reliable signals of malignancy in circulation for accurate GBM diagnosis. So far, there are no clinically validated circulating biomarkers available for GBM. Therefore, a marker-free approach was essential since conventional liquid biopsy relying on isolation methodology was not viable. Additionally, a mechanism capable of trace-level detection was crucial to detecting the rare GBM CSC-EVs from the complex environment in circulation. To break these barriers, we applied an ultrasensitive superlattice sensor, self-functionalized for surface-enhanced Raman scattering (SERS), to obtain holistic molecular profiling of GBM CSC-EVs with a marker-free approach. The superlattice sensor exhibited substantial SERS enhancement and ultralow limit of detection (LOD of attomolar 10 -18 M concentration) essential for trace-level detection of invisible GBM CSC-EVs directly from patient serum (without isolation). We detected as low as 5 EVs in 5 μL of solution, achieving the lowest LOD compared to existing SERS-based studies. We have experimentally demonstrated the crucial role of the signals of GBM CSC-EVs in the precise detection of glioblastoma. This was evident from the unique molecular profiles of GBM CSC-EVs demonstrating significant variation compared to noncancer EVs and EVs of GBM cancer cells, thus adding more clarity to the current understanding of GBM CSC-EVs. Preliminary validation of our approach was undertaken with a small amount of peripheral blood (5 μL) derived from GBM patients with 100% sensitivity and 97% specificity. Identification of the signals of GBM CSC-EV in clinical sera specimens demonstrated that our technology could be used for accurate GBM detection. Our technology has the potential to improve GBM liquid biopsy, including real-time surveillance of GBM evolution in patients upon clinical validation. This demonstration of liquid biopsy with GBM CSC-EV provides an opportunity to introduce a paradigm potentially impacting the current landscape of GBM diagnosis.

Human papillomavirus (HPV) is a common sexually transmitted infection, with over 40% prevalence in the US. Oropharyngeal cancers (OPCs) driven by high-risk HPV are increasing (up to 90%), with HPV vaccination being the only prevention available. The aim of this study was to investigate HPV vaccination among patients aged between 18 and 26 years old with at least one encounter at a large healthcare system and identify sociodemographic factors associated with vaccine initiation and completion. A cross-sectional retrospective study was conducted between 2018 and 2021, including 265,554 patients identified from the Clinical Data Warehouse. HPV vaccination status by age, sex, race/ethnicity, insurance type, primary care (PCP) visits in the past year, alcohol, tobacco, illicit drug use, and age at vaccination was examined. Overall, 33.6% of females and 25.4% of males have completed the HPV vaccine. Black Americans were 35% more likely to initiate the vaccine than White Americans but were less likely to complete the entire course. Overall, HPV vaccination prevalence was far below the Health People 2030 goal of 80%, especially in young males. This low rate is troubling, since many patients had a PCP visit and remained unvaccinated, which serves as a missed opportunity for vaccination.

The development of immune checkpoint inhibitors (ICIs) has revolutionized cancer therapy but only a fraction of patients benefits from this therapy. Model-informed drug development can be used to assess prognostic and predictive clinical factors or biomarkers associated with treatment response. Most pharmacometric models have thus far been developed using data from randomized clinical trials, and further studies are needed to translate their findings into the real-world setting. We developed a tumor growth inhibition model based on real-world clinical and imaging data in a population of 91 advanced melanoma patients receiving ICIs (i.e., ipilimumab, nivolumab, and pembrolizumab). Drug effect was modeled as an ON/OFF treatment effect, with a tumor killing rate constant identical for the three drugs. Significant and clinically relevant covariate effects of albumin, neutrophil to lymphocyte ratio, and Eastern Cooperative Oncology Group (ECOG) performance status were identified on the baseline tumor volume parameter, as well as NRAS mutation on tumor growth rate constant using standard pharmacometric approaches. In a population subgroup (n = 38), we had the opportunity to conduct an exploratory analysis of image-based covariates (i.e., radiomics features), by combining machine learning and conventional pharmacometric covariate selection approaches. Overall, we demonstrated an innovative pipeline for longitudinal analyses of clinical and imaging RWD with a high-dimensional covariate selection method that enabled the identification of factors associated with tumor dynamics. This study also provides a proof of concept for using radiomics features as model covariates., (© 2023 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Brigatinib, a next-generation anaplastic lymphoma kinase (ALK) inhibitor designed to overcome mechanisms of resistance associated with crizotinib, is approved for the treatment of ALK-positive advanced or metastatic non-small cell lung cancer. After oral administration of single doses of brigatinib 30-240 mg, the median time to reach maximum plasma concentration ranged from 1 to 4 h. In patients with advanced malignancies, brigatinib showed dose linearity over the dose range of 60-240 mg once daily. A high-fat meal had no clinically meaningful effect on systemic exposures of brigatinib (area under the plasma concentration-time curve); thus, brigatinib can be administered with or without food. In a population pharmacokinetic analysis, a three-compartment pharmacokinetic model with transit absorption compartments was found to adequately describe brigatinib pharmacokinetics. In addition, the population pharmacokinetic analyses showed that no dose adjustment is required based on body weight, age, race, sex, total bilirubin (< 1.5× upper limit of normal), and mild-to-moderate renal impairment. Data from dedicated phase I trials have indicated that no dose adjustment is required for patients with mild or moderate hepatic impairment, while a dose reduction of approximately 40% (e.g., from 180 to 120 mg) is recommended for patients with severe hepatic impairment, and a reduction of approximately 50% (e.g., from 180 to 90 mg) is recommended when administering brigatinib to patients with severe renal impairment. Brigatinib is primarily metabolized by cytochrome P450 (CYP) 3A, and results of clinical drug-drug interaction studies and physiologically based pharmacokinetic analyses have demonstrated that coadministration of strong or moderate CYP3A inhibitors or inducers with brigatinib should be avoided. If coadministration with a strong or moderate CYP3A inhibitor cannot be avoided, the dose of brigatinib should be reduced by approximately 50% (strong CYP3A inhibitor) or approximately 40% (moderate CYP3A inhibitor), respectively. Brigatinib is a weak inducer of CYP3A in vivo; data from a phase I drug-drug interaction study showed that coadministration of brigatinib 180 mg once daily reduced the oral midazolam area under the plasma concentration-time curve from time zero to infinity by approximately 26%. Brigatinib did not inhibit CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, or CYP2D6 at clinically relevant concentrations in vitro. Exposure-response analyses based on data from the ALTA (ALK in Lung Cancer Trial of AP26113) and ALTA-1L pivotal trials of brigatinib confirm the favorable benefit versus risk profile of the approved titration dosing regimen of 180 mg once daily (after a 7-day lead-in at 90 mg once daily)., (© 2023. The Author(s).)

Optimizing Ponatinib Treatment in CP-CML (OPTIC) was a randomized, phase II dose-optimization trial of ponatinib in chronic phase-chronic myeloid leukemia (CP-CML) resistant to ≥ 2 tyrosine kinase inhibitors or with T315I mutation. Patients were randomized to starting doses of 45-, 30-, or 15-mg ponatinib once daily. Patients receiving 45- or 30-mg reduced to 15-mg upon achievement of ≤ 1% BCR::ABL1 IS (≥ molecular response with 2-log reduction (MR2)). The exposure-molecular response relationship was described using a four-state, discrete-time Markov model. Time-to-event models were used to characterize the relationship between exposure and arterial occlusive events (AOEs), grade ≥ 3 neutropenia, and thrombocytopenia. Increasing systemic exposures were associated with increasing probability of transitioning from no response to ≥ MR1, and from MR1 to ≥ MR1, with odds ratios of 1.63 (95% confidence interval (CI), 1.06-2.73) and 2.05 (95% CI, 1.53-2.89) for a 15-mg dose increase, respectively. Ponatinib exposure was a significant predictor of AOEs (hazard ratio (HR) 2.05, 95% CI, 1.43-2.93, for a 15-mg dose increase). In the exposure-safety models for neutropenia and thrombocytopenia, exposure was a significant predictor of grade ≥ 3 thrombocytopenia (HR 1.31, 95% CI, 1.05-1.64, for a 15-mg dose increase). Model-based simulations predicted a clinically meaningful higher rate of ≥ MR2 response at 12 months for the 45-mg starting dose (40.4%) vs. 30-mg (34%) and 15-mg (25.2%). The exposure-response analyses supported a ponatinib starting dose of 45 mg with reduction to 15 mg at response for patients with CP-CML., (© 2023 Takeda Pharmaceutical. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Aims: To examine the association between COVID-19 Shutdown and within-subjects changes in body weight, body mass index (BMI), and glycemic parameters using electronic health record (EHR) data from 23,000 adults with type 2 diabetes (T2DM)., Methods: Patients with T2DM with outpatient visit data on body weight, BMI, hemoglobin A1c (HbA1c), and blood glucose (≥ 2 measures before and after 3/16/2020) recorded in the EHR at the University of Pittsburgh Medical Center were included. A within-subjects analysis compared average and clinically significant changes in weight, BMI, HbA1c, and blood glucose during the year POST-Shutdown (Time 2-3) compared to the same interval during the PRE-Shutdown year (Time 0-1) using paired samples t-tests and the McNemar-Bowker test., Results: We studied 23,697 adults with T2DM (51% female; 89% White; mean age = 66 ± 13 years; mean BMI = 34 ± 7 kg/m 2 ; mean HbA1c = 7 ± 2% [53 ± 21.9 mmol/mol]). Weight and BMI decreased during both the PRE- and POST-Shutdown intervals, but the changes were statistically smaller during the year POST-Shutdown relative to PRE (0.32 kg and 0.11 units, p < 0.0001). HbA1c showed statistically greater improvements during the POST-Shutdown interval compared to PRE (- 0.18% [-2 mmol/mol], p < 0.0001), but changes in glucose did not differ for the two intervals., Conclusions: Despite widespread discussion of weight gain in association with the COVID-19 Shutdown, study data showed no evidence of adverse effects of Shutdown on body weight, BMI, HbA1C, or blood glucose in a large sample of adults with T2DM. This information may help to inform future public health decision-making., (© 2023. Springer-Verlag Italia S.r.l., part of Springer Nature.)

Brigatinib is an anaplastic lymphoma kinase (ALK) inhibitor approved for the treatment of ALK-positive non-small cell lung cancer. This open-label, parallel-group study investigated the effect of chronic hepatic impairment on the pharmacokinetics (PK) of brigatinib to inform dosing recommendations for these patients. Participants with hepatic impairment classified according to Child-Pugh categories of mild (A), moderate (B), or severe (C) and matched-healthy participants with normal hepatic function received a single oral dose of 90-mg brigatinib. Plasma samples were collected for the determination of brigatinib plasma protein binding and estimation of plasma PK parameters. Twenty-seven participants were enrolled (Child-Pugh A-C, n = 6 each; matched-healthy participants, n = 9). The mean fraction of free plasma brigatinib was comparable for the Child-Pugh A (11.1%), Child-Pugh B (10.8%), and healthy participant groups (8.5%); free brigatinib was higher in the Child-Pugh C group (23.1%). There were no clinically meaningful effects of mild or moderate hepatic impairment on unbound systemic exposures (area under the plasma concentration-time curve [AUC]) of brigatinib (geometric least-squares mean ratios [90% CI] of 89.32% [69.79%-114.31%] and 99.55% [77.78%-127.41%], respectively). In the severe hepatic impairment group, brigatinib unbound AUC was approximately 37% higher (geometric least-squares mean ratio [90% CI] of 137.41% [107.37%-175.86%]) compared with healthy participants with normal hepatic function. Brigatinib was well tolerated in healthy participants and in participants with hepatic impairment. No dose adjustment is required for patients with mild or moderate hepatic impairment. The brigatinib dose should be reduced by approximately 40% for patients with severe hepatic impairment., (© 2023. The Author(s).)

Natural killer (NK) cells undergo multiple DNA genomic alterations, especially methylation-based modifications that affect activation and function. Several epigenetic modifier markers have been targeted for immunotherapy to date, but the possibility of cancer diagnosis using NK cell's DNA has been overlooked. Here, we investigated the potential use of NK cell DNA genome modifications as markers for the diagnosis of colorectal cancer (CRC) and validated their efficacy in CRC patients. Using Raman spectroscopy as the detection methodology, we identified CRC-specific methylation signatures by comparing CRC-interacted NK cells to healthy circulating NK cells. Subsequently, we identified methylation-dependent alterations in these NK cell populations. These markers were then utilized by a machine learning algorithm to develop a diagnostic model with predictive capabilities. The diagnostic prediction model accurately differentiated CRC patients from normal controls. Our findings demonstrated the utility of NK DNA markers in the diagnosis of CRC.

Lung cancer is one of the most common cancers with high mortality worldwide despite the development of molecularly targeted therapies and immunotherapies. A significant challenge in managing lung cancer is the accurate diagnosis of cancerous lesions owing to the lack of sensitive and specific biomarkers. The current procedure necessitates an invasive tissue biopsy for diagnosis and molecular subtyping, which presents patients with risk, morbidity, anxiety, and high false-positive rates. The high-risk diagnostic approach has highlighted the need to search for a reliable, low-risk noninvasive diagnostic approach to capture lung cancer heterogeneity precisely. The immune interaction profile of lung cancer is driven by immune cells' distinctive, precise interactions with the tumor microenvironment. Here, we hypothesize that immune cells, particularly T cells, can be used for accurate lung cancer diagnosis by exploiting the distinctive immune-tumor interaction by detecting the immune-diagnostic signature. We have developed an ultrasensitive T-sense nanosensor to probe these specific diagnostic signatures using the physical synthesis process of multiphoton ionization. Our research employed predictive in vitro models of lung cancers, cancer-associated T cells (PCAT, MCAT) and CSC-associated T cells (PCSCAT, MCSCAT), from primary and metastatic lung cancer patients to reveal the immune-diagnostic signature and uncover the molecular, functional, and phenotypic separation between patient-derived T cells (PDT) and healthy samples. We demonstrated this by adopting a machine learning model trained with SERS data obtained using cocultured T cells with preclinical models (CAT, CSCAT) of primary (H69AR) and metastatic lung cancer (H1915). Interrogating these distinct signatures with PDT captured the complexity and diversity of the tumor-associated T cell signature across the patient population, exposing the clinical feasibility of immune diagnosis in an independent cohort of patient samples. Thus, our predictive approach using T cells from the patient peripheral blood showed a highly accurate diagnosis with a specificity and sensitivity of 94.1% and 100%, respectively, for primary lung cancer and 97.9% and 94.4% for metastatic lung cancer. Our results prove that the immune-diagnostic signature developed in this study could be used as a clinical technology for cancer diagnosis and determine the course of clinical management with T cells.

The recent COVID-19 pandemic has highlighted the inadequacies of existing diagnostic techniques and the need for rapid and accurate diagnostic systems. Although molecular tests such as RT-PCR are the gold standard, they cannot be employed as point-of-care testing systems. Hence, a rapid, noninvasive diagnostic technique such as Surface-enhanced Raman scattering (SERS) is a promising analytical technique for rapid molecular or viral diagnosis. Here, we have designed a SERS- based test to rapidly diagnose SARS-CoV-2 from saliva. Physical methods synthesized the nanostructured sensor. It significantly increased the detection specificity and sensitivity by ~ten copies/mL of viral RNA (~femtomolar concentration of nucleic acids). Our technique combines the multiplexing capability of SERS with the sensitivity of novel nanostructures to detect whole virus particles and infection-associated antibodies. We have demonstrated the feasibility of the test with saliva samples from individuals who tested positive for SARS-CoV-2 with a specificity of 95%. The SERS-based test provides a promising breakthrough in detecting potential mutations that may come up with time while also preparing the world to deal with other pandemics in the future with rapid response and very accurate results.

The purpose of this study was to assess the effect of pevonedistat, a neural precursor cell expressed, developmentally down-regulated protein 8 (NEDD8)-activating enzyme inhibitor, on the heart rate-corrected QT (QTc) interval in cancer patients. Patients were randomized 1:1 to receive pevonedistat 25 or 50 mg/m 2 on day 1 and the alternate dose on day 8. Triplicate electrocardiograms were collected at intervals over 0-11 hours and at 24 hours via Holter recorders on days -1 (baseline), 1, and 8. Changes from time-matched baseline values were calculated for QTc by Fridericia (QTcF), PR, and QRS intervals. Serial time-matched blood samples for analysis of pevonedistat plasma pharmacokinetics were collected and a concentration-QTc analysis conducted. Safety was assessed by monitoring vital signs, physical examinations, and clinical laboratory tests. Forty-four patients were included in the QTc analysis. Maximum least square (LS) mean increase from time-matched baseline in QTcF was 3.2 milliseconds at 1 hour postdose for pevonedistat at 25 mg/m 2 , while the LSs mean change from baseline in QTcF was -1.7 milliseconds 1 hour postdose at 50 mg/m 2 . The maximum 2-sided 90% upper confidence bound was 6.7 and 2.9 milliseconds for pevonedistat at 25 and 50 mg/m 2 , respectively. Pevonedistat did not result in clinically relevant effects on heart rate, nor on PR or QRS intervals. Results from pevonedistat concentration-QTc analysis were consistent with these findings. Administration of pevonedistat to cancer patients at a dose of up to 50 mg/m 2 showed no evidence of QT prolongation, indicative of the lack of clinically meaningful effects on cardiac repolarization. ClinicalTrials.gov identifier: NCT03330106 (first registered on November 6, 2017)., (© 2022 Takeda Pharmaceutical and The Authors. Clinical Pharmacology in Drug Development published by Wiley Periodicals LLC on behalf of American College of Clinical Pharmacology.)

Systemic lupus erythematosus (SLE) is an autoimmune disease affecting multiple organ systems. Many investigational agents have failed or shown only modest effects when added to standard of care (SoC) therapy in placebo-controlled trials, and only two therapies have been approved for SLE in the last 60 years. Clinical trial outcomes have shown discordance in drug effects between clinical endpoints. Herein, we characterized longitudinal disease activity in the SLE population and the sources of variability by developing a latent disease trajectory model for SLE component endpoints (Systemic Lupus Erythematosus Disease Activity Index [SLEDAI], Physician's Global Assessment [PGA], British Isles Lupus Assessment Group Index [BILAG]) and composite endpoints (Systemic Lupus Erythematosus Responder Index [SRI], BILAG-based Composite Lupus Assessment [BICLA], and Lupus Low Disease Activity State [LLDAS]) using patient-level historical SoC data from nine phase II and III studies. Across all endpoints, in predictions up to 52 weeks from the final disease trajectory model, the following baseline covariates were associated with a greater decrease in SLE disease activity and higher response to placebo + SoC: Hispanic ethnicity from Central/South America, absence of hypocomplementemia, recent SLE diagnosis, and high baseline disease activity score using SLEDAI and BILAG separately. No discernible differences were observed in the trajectory of response to placebo + SoC across different SoC medications (antimalarial and immunosuppressant such as mycophenolate, methotrexate, and azathioprine). Across all endpoints, disease trajectory showed no difference in Asian versus non-Asian patients, supporting Asia-inclusive global SLE drug development. These results describe the first population approach to support a model-informed drug development framework in SLE., (© 2022 EMD Serono Research & Development Institute. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Access lag to innovative therapies in Asian populations continues to present a challenge to global health. Recent progressive changes in the global regulatory landscape, including newer guidelines, are enabling simultaneous global drug development and near-simultaneous global drug registration. The International Conference on Harmonization (ICH) E17 guideline outlines general principles for the design and analysis of multiregional clinical trials (MRCTs). We posit that translational research and quantitative clinical pharmacology tools are core enablers for Asia-inclusive global drug development aligned with ICH E17 principles. Assessment of ethnic sensitivity should be initiated early in the development lifecycle to inform the need for, and extent of, Asian phase I ethno-bridging data. Relevant ethno-bridging data may be generated as standalone Asian phase I trials, as part of Western First-In-Human trials, or under accelerated development settings as a lead-in phase in an MRCT. Quantitative understanding of human clearance mechanisms and pharmacogenetic factors is vital to forecasting ethnic sensitivity in drug exposure using physiologically-based pharmacokinetic models. Stratification factors to control heterogeneity in MRCTs can be identified by reverse translational research incorporating pharmacometric disease models and model-based meta-analyses. Because epidemiological variations can extend to the molecular level, quantitative systems pharmacology models may be useful in forecasting how molecular variation in therapeutic targets or pathway proteins across populations might impact treatment outcomes. Through prospective evaluation of conservation in drug- and disease-related intrinsic and extrinsic factors, a pooled East Asian region can be implemented in Asia-inclusive MRCTs to maximize efficiency in substantiating evidence of benefit-risk for the region at-large with a Totality of Evidence approach., (© 2022 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)