Venetoclax in biomarker-selected multiple myeloma patients: Impact of exposure on clinical efficacy and safety.

Venetoclax, a potent BCL-2 inhibitor, is currently under development for treatment of t(11;14) Multiple myeloma (MM). The objective of this research was to investigate the exposure-response relationships of venetoclax for a phase 1/2 study evaluating venetoclax monotherapy or in combination with dexamethasone in relapsed or refractory MM. A total of 117 patients receiving venetoclax at 300, 600, 800, 900, or 1200 mg were included in the analysis. The impact of venetoclax exposures on efficacy (objective response rate [ORR], progression-free survival [PFS] and overall survival [OS]) as well as safety (treatment-emergent adverse effects (grade ≥3) of neutropenia, infection, and any grade of serious treatment-emergent adverse effects) was evaluated. In the t(11;14)-positive subpopulation, venetoclax exposure relationships to PFS and OS indicated a trend of longer PFS and OS with higher exposures. Moreover, logistic regression analyses for clinical response (ORR and ≥VGPR rate) demonstrated a statistically significant (p < 0.05) relationship with exposure. Evaluation of the exposure-safety relationships demonstrated a lack of a relationship between venetoclax exposures (AUCavg ) and grade ≥3 infections, grade ≥3 neutropenia, grade ≥3 treatment-emergent adverse events or any grade serious treatment-emergent adverse events. These findings support further study of venetoclax at 800 mg QD dose in combination with dexamethasone in the t(11;14)-positive patient population where increased efficacy was observed without an increase in safety events.Clinical Trial: NCT01794520 registered 20 February 2013.

Use of Clinical Trial Simulations to Compare the Performance of Different Approaches for Population Analyses of Pediatric Pharmacokinetic Data.

The adequate characterization of the pharmacokinetics of a drug used in pediatrics is a mainstay of pediatric development programs and is critical for accurate dose selection in pediatrics. Analysis approaches can impact the estimation and characterization of pediatric pharmacokinetic parameters. Simulations were conducted to compare the performance of different approaches for analyzing pediatric pharmacokinetic data in the presence of extensive data from adult studies. Simulated clinical trial datasets were generated encompassing different scenarios that might be encountered in pediatric drug development. For each scenario, 250 clinical trials were simulated and analyzed using each of the following approaches: (1) estimating pediatric parameters using only pediatric data; (2) fixing specific parameters to adult estimates and estimating the remaining pediatric parameters using only pediatric data; (3) estimating pediatric parameters using adult parameters as informative Bayesian priors; (4) estimating pediatric parameters using combined adult and pediatric datasets with exponents for body weight effects estimated using adult and pediatric data; and (5) estimating pediatric parameters using combined adult and pediatric datasets with exponents for body weight effects estimated using pediatric data only. Each analysis approach was evaluated for its success in the estimation of true pediatric pharmacokinetic parameter values. Results demonstrated that analyzing pediatric data using a Bayesian approach generally performed best and had the lowest probability of significant bias in the estimated pediatric pharmacokinetic parameters among different scenarios evaluated. This clinical trial simulation framework can be used to inform the optimal approach for analyses of pediatric data for other pediatric drug development program scenarios beyond the cases evaluated in these analyses.

Upadacitinib pharmacokinetics and exposure-response analyses of efficacy and safety in psoriatic arthritis patients - Analyses of phase III clinical trials.

Upadacitinib is an oral Janus kinase inhibitor approved for the treatment of rheumatoid arthritis (RA) and recently approved by the European Medicines Agency for the treatment of psoriatic arthritis (PsA). The efficacy and safety profile of upadacitinib in PsA have been established in the SELECT-PsA program in two global phase III studies, which evaluated upadacitinib 15 and 30 mg q.d. The analyses described here characterized upadacitinib pharmacokinetics and exposure-response relationships for efficacy and safety endpoints using data from the SELECT-PsA studies. Upadacitinib pharmacokinetics in patients with PsA were characterized through a Bayesian population analysis approach and were comparable to pharmacokinetics in patients with RA. Exposure-response relationships for key efficacy and safety endpoints were characterized using data from 1916 patients with PsA. The percentage of patients achieving efficacy endpoints at week 12 (American College of Rheumatology [ACR]50 and ACR70), 16 and 24 (sIGA0/1) increased with increasing upadacitinib average plasma concentration over a dosing interval, whereas no clear exposure-response trend was observed for ACR20 at week 12 or ACR20/50/70 at week 24 within the range of plasma exposures evaluated in the phase III PsA studies. No clear trends for exposure-response relationships were identified for experiencing pneumonia, herpes zoster infection, hemoglobin less than 8 g/dl, lymphopenia (grade ≥ 3), or neutropenia (grade ≥ 3) after 24 weeks of treatment. Shallow relationships with plasma exposures were observed for serious infections and hemoglobin decrease greater than 2 g/dl from baseline at week 24. Based on exposure-response analyses, the upadacitinib 15 mg q.d. regimen is predicted to achieve robust efficacy in patients with PsA and to be associated with limited incidences of reductions in hemoglobin or occurrence of serious infections.

SEEDS: data driven inference of structural model errors and unknown inputs for dynamic systems biology.

SUMMARY: Dynamic models formulated as ordinary differential equations can provide information about the mechanistic and causal interactions in biological systems to guide targeted interventions and to design further experiments. Inaccurate knowledge about the structure, functional form and parameters of interactions is a major obstacle to mechanistic modeling. A further challenge is the open nature of biological systems which receive unknown inputs from their environment. The R-package SEEDS implements two recently developed algorithms to infer structural model errors and unknown inputs from output measurements. This information can facilitate efficient model recalibration as well as experimental design in the case of misfits between the initial model and data. AVAILABILITY AND IMPLEMENTATION: For the R-package seeds, see the CRAN server https://cran.r-project.org/package=seeds.

Model Informed Dosing Regimen and Phase I Results of the Anti-PD-1 Antibody Budigalimab (ABBV-181).

Budigalimab is a humanized, recombinant, Fc mutated IgG1 monoclonal antibody targeting programmed cell death 1 (PD-1) receptor, currently in phase I clinical trials. The safety, efficacy, pharmacokinetics (PKs), pharmacodynamics (PDs), and budigalimab dose selection from monotherapy dose escalation and multihistology expansion cohorts were evaluated in patients with previously treated advanced solid tumors who received budigalimab at 1, 3, or 10 mg/kg intravenously every 2 weeks (Q2W) in dose escalation, including Japanese patients that received 3 and 10 mg/kg Q2W. PK modeling and PK/PD assessments informed the dosing regimen in expansion phase using data from body-weight-based dosing in the escalation phase, based on which patients in the multihistology expansion cohort received flat doses of 250 mg Q2W or 500 mg every four weeks (Q4W). Immune-related adverse events (AEs) were reported in 11 of 59 patients (18.6%), of which 1 of 59 (1.7%) was considered grade ≥ 3 and the safety profile of budigalimab was consistent with other PD-1 targeting agents. No treatment-related grade 5 AEs were reported. Four responses per Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 were reported in the dose escalation cohort and none in the multihistology expansion cohort. PK of budigalimab was approximately dose proportional and sustained > 99% peripheral PD-1 receptor saturation was observed by 2 hours postdosing, across doses. PK/PD and safety profiles were comparable between Japanese and Western patients, and exposure-safety analyses did not indicate any trends. Observed PK and PD-1 receptor saturation were consistent with model predictions for flat doses and less frequent regimens, validating the early application of PK modeling and PK/PD assessments to inform the recommended dose and regimen, following dose escalation.

Modelling and mathematical analysis of the M$_{2}$ receptor-dependent joint signalling and secondary messenger network in CHO cells.

The muscarinic M$_{2}$ receptor is a prominent member of the GPCR family and strongly involved in heart diseases. Recently published experimental work explored the cellular response to iperoxo-induced M$_{2}$ receptor stimulation in Chinese hamster ovary (CHO) cells. To better understand these responses, we modelled and analysed the muscarinic M$_{2}$ receptor-dependent signalling pathway combined with relevant secondary messenger molecules using mass action. In our literature-based joint signalling and secondary messenger model, all binding and phosphorylation events are explicitly taken into account in order to enable subsequent stoichiometric matrix analysis. We propose constraint flux sampling (CFS) as a method to characterize the expected shift of the steady state reaction flux distribution due to the known amount of cAMP production and PDE4 activation. CFS correctly predicts an experimentally observable influence on the cytoskeleton structure (marked by actin and tubulin) and in consequence a change of the optical density of cells. In a second step, we use CFS to simulate the effect of knock-out experiments within our biological system, and thus to rank the influence of individual molecules on the observed change of the optical cell density. In particular, we confirm the relevance of the protein RGS14, which is supported by current literature. A combination of CFS with Elementary Flux Mode analysis enabled us to determine the possible underlying mechanism. Our analysis suggests that mathematical tools developed for metabolic network analysis can also be applied to mixed secondary messenger and signalling models. This could be very helpful to perform model checking with little effort and to generate hypotheses for further research if parameters are not known.

A Bayesian approach to estimating hidden variables as well as missing and wrong molecular interactions in ordinary differential equation-based mathematical models.

Ordinary differential equations (ODEs) are a popular approach to quantitatively model molecular networks based on biological knowledge. However, such knowledge is typically restricted. Wrongly modelled biological mechanisms as well as relevant external influence factors that are not included into the model are likely to manifest in major discrepancies between model predictions and experimental data. Finding the exact reasons for such observed discrepancies can be quite challenging in practice. In order to address this issue, we suggest a Bayesian approach to estimate hidden influences in ODE-based models. The method can distinguish between exogenous and endogenous hidden influences. Thus, we can detect wrongly specified as well as missed molecular interactions in the model. We demonstrate the performance of our Bayesian dynamic elastic-net with several ordinary differential equation models from the literature, such as human JAK-STAT signalling, information processing at the erythropoietin receptor, isomerization of liquid α-Pinene, G protein cycling in yeast and UV-B triggered signalling in plants. Moreover, we investigate a set of commonly known network motifs and a gene-regulatory network. Altogether our method supports the modeller in an algorithmic manner to identify possible sources of errors in ODE-based models on the basis of experimental data.

Learning (from) the errors of a systems biology model.

Mathematical modelling is a labour intensive process involving several iterations of testing on real data and manual model modifications. In biology, the domain knowledge guiding model development is in many cases itself incomplete and uncertain. A major problem in this context is that biological systems are open. Missed or unknown external influences as well as erroneous interactions in the model could thus lead to severely misleading results. Here we introduce the dynamic elastic-net, a data driven mathematical method which automatically detects such model errors in ordinary differential equation (ODE) models. We demonstrate for real and simulated data, how the dynamic elastic-net approach can be used to automatically (i) reconstruct the error signal, (ii) identify the target variables of model error, and (iii) reconstruct the true system state even for incomplete or preliminary models. Our work provides a systematic computational method facilitating modelling of open biological systems under uncertain knowledge.