Model-based meta-analysis using latent variable modeling to set benchmarks for new treatments of systemic lupus erythematosus.

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.

Disease trajectory of SLE clinical endpoints and covariates affecting disease severity and probability of response: Analysis of pooled patient-level placebo (Standard-of-Care) data to enable model-informed drug development.

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.

Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities.

The exponential increase in our ability to harness multi-dimensional biological and clinical data from experimental to real-world settings has transformed pharmaceutical research and development in recent years, with increasing applications of artificial intelligence (AI) and machine learning (ML). Patient-centered iterative forward and reverse translation is at the heart of precision medicine discovery and development across the continuum from target validation to optimization of pharmacotherapy. Integration of advanced analytics into the practice of Translational Medicine is now a fundamental enabler to fully exploit information contained in diverse sources of big data sets such as "omics" data, as illustrated by deep characterizations of the genome, transcriptome, proteome, metabolome, microbiome, and exposome. In this commentary, we provide an overview of ML applications in drug discovery and development, aligned with the three strategic pillars of Translational Medicine (target, patient, dose) and offer perspectives on their potential to transform the science and practice of the discipline. Opportunities for integrating ML approaches into the discipline of Pharmacometrics are discussed and will revolutionize the practice of model-informed drug discovery and development. Finally, we posit that joint efforts of Clinical Pharmacology, Bioinformatics, and Biomarker Technology experts are vital in cross-functional team settings to realize the promise of AI/ML-enabled Translational and Precision Medicine.

Challenges in Drug Development Posed by the COVID-19 Pandemic: An Opportunity for Clinical Pharmacology.

The unprecedented challenges posed by the coronavirus disease 2019 (COVID-19) pandemic highlight the urgency for applying clinical pharmacology and model-informed drug development in (i) dosage optimization for COVID-19 therapies, (ii) approaching therapeutic dilemmas in clinical trial settings, and (iii) maximizing value of information from impacted non-COVID-19 trials. More than ever, we have a responsibility for adaptive evidence synthesis with a Totality of Evidence mindset in this race against time across biomedical research, clinical practice, drug development, and regulation.

High-Throughput, Sensitive LC-MS Quantification of Biotherapeutics and Biomarkers Using Antibody-Free, Peptide-Level, Multiple-Mechanism Enrichment via Strategic Regulation of pH and Ionic and Solvent Strengths.

Sensitive and high-throughput measurement of biotherapeutics and biomarkers in plasma and tissues is critical for protein-drug development. Enrichment of target signature peptide (SP) after sample digestion permits sensitive LC-MS-based protein quantification and carries several prominent advantages over protein-level enrichment; however, developing high-quality antipeptide antibodies is challenging. Here we describe a novel, antibody-free, peptide-level-enrichment technique enabling high-throughput, sensitive, and robust quantification of proteins in biomatrices, by highly selective removal of matrix peptides and components via cation-exchange (CX) reversed-phase (RP) SPE with strategically regulated pH and ionic and organic strengths. Multiple-mechanism washing and elution achieved highly selective separation despite the low plate number of the SPE cartridge. We first investigated the adsorption-desorption behaviors of peptides on CX-RP sorbent and the coexisting, perplexing effects of pH, and ionic and organic strengths on the selectivity for SP enrichment, which has not been previously characterized. We demonstrated that the selectivity for separating target SPs from matrix peptides was closely associated with buffer pH relative to the pI of the SP, and pH values of pI - 2, pI, and pI + 2 respectively provided exceptional specificity for the ionic wash, the hydrophobic wash, and selective elution. Furthermore, desorption of peptides from the mixed-mode sorbent showed exponential and linear dependence, respectively, on organic-solvent percentage and salt percentage. On the basis of these findings, we established a streamlined procedure for rapid and robust method development. Quantification of biotherapeutics, targets, and biomarkers in plasma and tissues was used as the model system. Selective enrichment of target SPs was achieved along with elimination of 87-95% of matrix peptides, which improved the LOQ by 20-fold (e.g., 2 ng per gram of tissue). Application was demonstrated by sensitive quantification of time courses of mAb (T84.66) and target (CEA) in plasma and tumor tissues from a low-dose mouse PK study. For the first time, down-regulation of membrane-associated antigen following mAb treatment was observed. The CX-RP enrichment is robust, high-throughput, and universally applicable and thus is highly valuable for ultrasensitive, large-scale measurement of target protein in plasma and tissues.

Application of a MABEL Approach for a T-Cell-Bispecific Monoclonal Antibody: CEA TCB.

CEA TCB is a novel T-cell-bispecific (TCB) antibody targeting the carcinoembryonic antigen (CEA) expressed on tumor cells and the CD3 epsilon chain (CD3e) present on T cells, which is currently in Phase 1 clinical trials (NCT02324257) for the treatment of CEA-positive solid tumors. Because the human CEA (hCEA) binder of CEA TCB does not cross-react with cynomolgus monkey and CEA is absent in rodents, alternative nonclinical safety evaluation approaches were considered. These included the development of a cynomolgus monkey cross-reactive homologous (surrogate) antibody (cyCEA TCB) for its evaluation in cynomolgus monkey and the development of double-transgenic mice, expressing hCEA and human CD3e (hCEA/hCD3e Tg), as a potential alternative species for nonclinical safety studies. However, a battery of nonclinical in vitro/ex vivo experiments demonstrated that neither of the previous approaches provided a suitable and pharmacologically relevant model to assess the safety of CEA TCB. Therefore, an alternative approach, a minimum anticipated biological effect level (MABEL), based on an in vitro tumor lysis assay was used to determine the starting dose for the first-in-human study. Using the most conservative approach to the MABEL assessment, a dose of 52 µg was selected as a safe starting dose for clinical study.

Minipig as a potential translatable model for monoclonal antibody pharmacokinetics after intravenous and subcutaneous administration.

Subcutaneous (SC) delivery is a common route of administration for therapeutic monoclonal antibodies (mAbs) with pharmacokinetic (PK)/pharmacodynamic (PD) properties requiring long-term or frequent drug administration. An ideal in vivo preclinical model for predicting human PK following SC administration may be one in which the skin and overall physiological characteristics are similar to that of humans. In this study, the PK properties of a series of therapeutic mAbs following intravenous (IV) and SC administration in Göttingen minipigs were compared with data obtained previously from humans. The present studies demonstrated: (1) minipig is predictive of human linear clearance; (2) the SC bioavailabilities in minipigs are weakly correlated with those in human; (3) minipig mAb SC absorption rates are generally higher than those in human and (4) the SC bioavailability appears to correlate with systemic clearance in minipigs. Given the important role of the neonatal Fc-receptor (FcRn) in the PK of mAbs, the in vitro binding affinities of these IgGs against porcine, human and cynomolgus monkey FcRn were tested. The result showed comparable FcRn binding affinities across species. Further, mAbs with higher isoelectric point tended to have faster systemic clearance and lower SC bioavailability in both minipig and human. Taken together, these data lend increased support for the use of the minipig as an alternative predictive model for human IV and SC PK of mAbs.

Interspecies scaling: prediction of human biliary clearance and comparison with QSPKR.

The aim of this study was to evaluate the prediction performance of various allometric scaling methods in predicting human biliary clearance (CL(b)) from data in rats or multiple animal species and to compare the prediction performance with that of quantitative structure pharmacokinetic relationship (QSPKR) models. CL(b) data of parent drugs in rats and humans were collected from the literature for 18 compounds. A simple allometric approach was applied to CL(b) or unbound CL(b) using 0.75 or 0.66 as the allometric exponent. For scaling from rat studies alone, the prediction using 0.66 as the exponent was better than that using 0.75, and a better prediction was obtained for unbound CL(b) than CL(b). For a subset of compounds, six multiple-species scaling methods were compared, with the best prediction achieved with the simple unbound CL(b) approach. However, in the absence of protein binding data, the correction with maximum life-span potential (MLP) or 'Rule of exponent' (ROE) method offered the best prediction. Overall, multiple species had better predictability than scaling with the rat alone. Comparison of predicted human CL(b) values using multiple animal species and QSPKR offered similar prediction performance. In conclusion, the results of the present study, although based on limited data, suggested that the prediction for human CL(b) by allometry was greatly improved by the incorporation of protein binding. Human CL(b) prediction using rat data alone was not satisfactory. Additionally, QSPKR provides an alternative approach to allometry for the prediction of human biliary clearance.

Clinical pharmacokinetics and gastrointestinal tolerability of a novel extended-release microsphere formulation of azithromycin.

BACKGROUND AND OBJECTIVE: A novel oral, extended-release, microsphere formulation of azithromycin (AZSR) was developed to improve the gastrointestinal tolerability profile while allowing administration of an entire treatment course of azithromycin in a single dose. Several phase I clinical pharmacology studies were conducted to (i) identify a well-tolerated single-dose formulation that met a predefined exposure target; and (ii) evaluate the effect of food and antacid on the absorption of this formulation. Of these, five pivotal studies are described here. METHODS: The pharmacokinetic profile of AZSR was compared with that of the commercially available immediate-release azithromycin formulation (AZM) in an open-label, crossover, single-dose study (Study A), and their gastrointestinal tolerability profiles were compared in an observer-blind, parallel group, single-dose study (Study B). The effects of food (a high-fat meal and a standard meal) and antacid (a single 20 mL dose of Maalox Regular Strength, containing magnesium hydroxide, aluminium hydroxide and simethicone) on the absorption of azithromycin from AZSR were evaluated in three separate open-label, crossover, single-dose studies (Studies C, D and E). Healthy adult subjects were enrolled in all five studies, and all subjects were evaluable for tolerability. The dose used for all azithromycin formulations was 2.0 g. Serum azithromycin concentrations were determined using a validated high-performance liquid chromatography/electrochemical detection method, and pharmacokinetic parameters were analysed using noncompartmental methods. RESULTS: 377 subjects received a single 2.0 g dose of azithromycin as AZSR and/or AZM in the five studies. Compared with AZM, AZSR had a slower absorption rate (57% decrease in the mean peak concentration [C(max)] and an approximate 2.5-hour delay in the time to reach C(max) [t(max)]), with a mean relative bioavailability of 82.8%, which met the predefined exposure target (at least 80% bioavailability relative to AZM). Compared with AZM, AZSR was associated with significantly lower rates of nausea and vomiting. A high-fat meal increased the mean area under the serum concentration-time curve [AUC] from time zero to 72 hours post-dose (AUC(72 h)) by 23% and increased the C(max) of azithromycin by 115%. A standard meal increased the mean C(max) by 119% but had no clinically significant effect on the AUC(72 h). AZSR appeared to be better tolerated in the fasted state than in the fed state. The AUC(72 h) and C(max) of AZSR were not significantly affected by co-administration with a single dose of antacid. CONCLUSIONS: The extended-release microsphere formulation of azithromycin, AZSR, allows administration of an entire therapeutic course of azithromycin as a well-tolerated single 2.0 g dose. This formulation should be administered on an empty stomach and can be co-administered with antacids.

Absolute oral bioavailability of traxoprodil in cytochrome P450 2D6 extensive and poor metabolisers.

BACKGROUND: Traxoprodil, a substituted 4-phenylpiperidine, is an N-methyl-D-aspartate (NMDA) receptor antagonist that is selective for receptors containing the NR2B subunit. In vivo and in vitro studies examining the disposition of traxoprodil have demonstrated that it is mainly metabolised by cytochrome P450 (CYP) 2D6, a major drug-metabolising enzyme that exhibits a genetic polymorphism. OBJECTIVE: To assess the single-dose absolute oral bioavailability of traxoprodil in healthy male volunteers phenotyped as either CYP2D6 extensive or poor metabolisers. METHODS: This was an open-label, three-way crossover study. Traxoprodil was administered as a single dose orally in solution of 50, 100 and 300mg and intravenously as a constant rate 2-hour infusion of 50 and 100mg. CYP2D6 phenotype was assigned following single-dose dextromethorphan administration. RESULTS: In poor metabolisers (n = 6), oral bioavailability was approximately 80% and was consistent with a liver extraction ratio of approximately 20% (plasma clearance of approximately 4 mL/min/kg) indicating near complete absorption. Following intravenous administration, the mean volume of distribution at steady state (V(ss)) was moderate (approximately 6.5 L/kg) and the mean elimination half-life (t((1/2))) was approximately 20 hours. Following oral administration the mean maximum plasma concentration (C(max)) and area under the plasma concentration-time curve from time zero to infinity (AUC(infinity)) increased approximately proportionally with dose. In extensive metabolisers (n = 11), oral bioavailability was dose-dependent and nonlinear. At the 100mg dose, the absolute oral bioavailability was approximately 39.5%. Overall, the oral bioavailability ranged from 22.8% to 62.1% and its estimation was confounded by large differences in plasma concentrations at oral doses without equivalent intravenous doses. Following intravenous administration, plasma clearance was high (approximately 27 mL/min/kg), the V(ss) was moderate (approximately 4 L/Kg) and the t((1/2)) was approximately 2-4 hours. Following oral administration the C(max) and AUC(infinity) increased more than proportionally with dose. Apparent oral clearance decreased with increasing oral dose. However, t((1/2)) was approximately the same at all doses (approximately 4 hours). CONCLUSION: The pharmacokinetics of traxoprodil were quite different in the two phenotypes. In extensive metabolisers, the oral bioavailability was nonlinear and dose-dependent, while in poor metabolisers, oral bioavailability appeared to be linear and dose-independent. Based on the pharmacokinetics in extensive and poor metabolisers, the nonlinear oral bioavailability in extensive metabolisers may be attributed to saturation of hepatic first-pass CYP2D6 metabolism. Thus, at a high oral dose, the impact of CYP2D6 metabolism on traxoprodil pharmacokinetics is minimal.

Pharmacokinetic and pharmacodynamic modeling of humanized anti-factor IX antibody (SB 249417) in humans.

BACKGROUND: SB 249417 is a humanized anti-factor IX/IXa antibody that, on administration to rats and monkeys, produces an immediate suppression of factor IX activity and prolongation of activated partial thromboplastin times (aPTT). OBJECTIVE: Our objective was to establish the pharmacokinetics of SB 249417 and to explore its effects on factor IX activity levels and aPTT in humans. METHODS: In this phase I, single-blind, randomized, placebo-controlled, parallel-group, single intravenous infusion study, individual and mean data from a total of 26 healthy volunteers at 5 dosing levels were analyzed. A 2-compartment pharmacokinetic model was used in the analysis of total SB 249417 concentration-time profiles. A modified indirect-response model was used, with the total concentration indirectly serving as the driving force for the suppression of free factor IX concentration (as assessed by factor IX activity). The aPTT was related to factor IX activity with a biexponential equation, and a population approach was used to generate posterior parameter estimates for the individual fittings. RESULTS: Mean parameter estimates from individual fittings are 0.092 L/kg for volume of distribution, 0.15 L/kg for steady-state volume of distribution, and 0.0021 L/kg per hour for systemic clearance. The model described well the factor IX activity and aPTT time course in response to SB 249417 at 5 dose levels. The estimated half-life of factor IX in blood was 21 hours. CONCLUSIONS: This model was stable and robust in fitting both mean and individual data. Endogenous factor IX baseline levels and dose were the major determinants of the decline in factor IX activity during the infusion period. Thereafter the recovery of factor IX activity was governed solely by the endogenous factor IX turnover rate.