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Epcoritamab is a CD3xCD20 bispecific antibody that activates T cells to kill CD20-expressing B cells. Epcoritamab is approved for the treatment of adults with different types of relapsed or refractory lymphoma in various geographies, including the United States, Europe, and Japan. Epcoritamab demonstrated an overall response rate of 63%, a complete response rate of 39%, and manageable safety with the approved dosing regimen (0.16-mg and 0.8-mg step-up doses and 48-mg full dose, with dosing every week in cycles 1-3, every 2 weeks in cycles 4-9, and every 4 weeks in cycles ≥ 10) in patients with relapsed or refractory large B-cell lymphoma from the phase 1/2 EPCORE® NHL-1 trial expansion through January 31, 2022. Exposure-efficacy analyses including the EPCORE NHL-1 and EPCORE NHL-3 trials revealed that higher exposure was associated with a higher overall response rate, complete response rate, progression-free survival, and overall survival. A potential plateau of efficacy was observed at 48 mg or above. The exposure-safety analyses of these trials did not identify any safety concerns with the approved dosing regimen. No associations were detected between exposure and safety endpoints. The step-up doses were clinically active and helped mitigate cytokine release syndrome risk at the subsequent full doses. Most initial responses (94%) were observed during the weekly dosing period, and most responders with large B-cell lymphoma maintained or improved their response during every 2 weeks and every 4 weeks dosing. Overall, these analyses support the approved single-agent epcoritamab 0.16/0.8/48-mg dosing regimen in relapsed or refractory large B-cell lymphoma., (© 2025 The Author(s). Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Background and Objectives: Epcoritamab is a CD3xCD20 bispecific antibody approved for the treatment of adults with different types of relapsed or refractory (R/R) B cell non-Hodgkin lymphoma (B-NHL) after ≥ 2 lines of systemic therapy. Here we report the first results from a population pharmacokinetic model-based analysis using data from 2 phase 1/2 clinical trials (EPCORE ® NHL-1, NCT03625037 and EPCORE NHL-3, NCT04542824) evaluating epcoritamab in patients with R/R B-NHL., Methods: Plasma concentration-time data included 6819 quantifiable pharmacokinetic samples from 327 patients with R/R B-NHL. A wide range of subcutaneous epcoritamab doses, 0.004-60 mg, was explored, with most patients (n = 298) following the approved dosing regimen: step-up dose (SUD) 1 of 0.16 mg on cycle 1 day 1 and SUD 2 of 0.8 mg on cycle 1 day 8, followed by a full dose of 48 mg administered weekly during cycles 1-3, biweekly in cycles 4-9, and every 4 weeks thereafter. Each cycle lasted 28 days. The data were analyzed using nonlinear mixed-effects modeling., Results: Quasisteady-state approximation of a two-compartment target-mediated drug disposition model with first-order absorption adequately characterized pharmacokinetics of epcoritamab following subcutaneous administration. After the first full dose and at the end of the weekly dosing regimen (end of cycle 3), the estimated median time to maximum concentration (t max ) was 4 and 2.3 days, respectively. Age and body weight were significant covariates on the pharmacokinetics of epcoritamab. The geometric mean (coefficient of variation [CV], %) of the apparent total volume of distribution was 25.6 L (82%) for patients with R/R large B cell lymphoma in EPCORE NHL-1. Epcoritamab elimination exhibited nonlinear characteristics, with exposure increasing more than proportionally over 1.5-48 mg doses. The geometric mean (CV%) values of apparent total clearance and terminal half-life were 0.53 L/day (40%) and 22 days (58%), respectively, at the end of cycle 3 for the 48 mg full dose. Clinical data analyses did not identify any association between assessed characteristics, including body weight or age, and clinical efficacy or safety. After accounting for body weight, no clinically significant differences in epcoritamab pharmacokinetics were observed for sex, race, renal or hepatic function, or other disease characteristics. Age was not found to significantly affect epcoritamab pharmacokinetic exposure. Antidrug antibodies developed in 4 (2.6%) of 156 evaluable patients treated with the approved 0.16/0.8/48 mg regimen. Antidrug antibody status did not affect epcoritamab pharmacokinetics., Conclusions: Epcoritamab pharmacokinetics in R/R B-NHL were well characterized by the population pharmacokinetic model. No dosage adjustments are recommended in subpopulations based on body weight, age, sex, race, mild-to-moderate renal impairment, or mild hepatic impairment. The risk of immunogenicity was low. These are the first published results of population pharmacokinetic modeling for epcoritamab., Competing Interests: Declarations. Funding: This study was funded by Genmab A/S and AbbVie. Conflict of interest: T.L., M.v.d.L., K.S., M.P., M.S., H.F., T.A., M.G., and S.X. are employees of Genmab. T.A. owns stock in Genmab. L.G. is an employee of QuantPharm LLC and is a paid consultant of Genmab. A.P. is an employee and stockholder of AbbVie. Availability of data and material: Deidentified individual participant data will not be available upon request for further analyses by external independent researchers. Aggregated clinical trial data from the trials are provided via publicly accessible study registries/databases as required by law. For more information, please contact clinicaltrials@genmab.com. Ethics approval: Both EPCORE NHL-1 and EPCORE NHL-3 protocols were approved by an institutional ethics committee before the start of the trials. The trials were conducted in compliance with the International Council for Harmonisation Good Clinical Practice E6(R2) guidelines, local guidelines (Japan Good Clinical Practice for EPCORE NHL-3), principles of the Declaration of Helsinki, and relevant regulatory requirements. Consent to participate: All patients reviewed and signed informed consent forms before enrollment. Consent for publication: Because of the nature of this study, consent to publish was waived. Author contributions: Conceptualization: T.L., A.P., M.G., and S.X. Methodology: T.L., L.G., and M.v.d.L. Formal analysis and investigation: T.L., L.G., M.v.d.L, M.S., and H.F. Writing, original draft preparation: T.L. and S.X. Funding acquisition: T.L. and M.G. Resources: K.S. and M.P. Supervision: T.L., M.G., and S.X. All authors participated in the critical review and revision of this manuscript and provided approval of the manuscript for submission. Code availability: Not applicable., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The evolution of complex genetic networks is shaped over the course of many generations through multiple mechanisms. These mechanisms can be broken into two predominant categories: adaptive forces, such as natural selection, and non-adaptive forces, such as recombination, genetic drift, and random mutation. Adaptive forces are influenced by the environment, where individuals better suited for their ecological niche are more likely to reproduce. This adaptive force results in a selective pressure which creates a bias in the reproduction of individuals with beneficial traits. Non-adaptive forces, in contrast, are not influenced by the environment: Random mutations occur in offspring regardless of whether they improve the fitness of the offspring. Both adaptive and non-adaptive forces play critical roles in the development of a species over time, and both forces are intrinsically linked to one another. We hypothesize that even under a simple sexual reproduction model, selective pressure will result in changes in the mutation rate and genome size. We tested this hypothesis by evolving Boolean networks using a modified genetic algorithm. Our results demonstrate that changes in environmental signals can result in selective pressure which affects mutation rate., Competing Interests: The authors declare no conflict of interest., (© 2021 The Authors.)

Fractal topologies, which are statistically self-similar over multiple length scales, are pervasive in nature. The recurrence of patterns in fractal-shaped branched objects, such as trees, lungs and sponges, results in a high surface area to volume ratio, which provides key functional advantages including molecular trapping and exchange. Mimicking these topologies in designed protein-based assemblies could provide access to functional biomaterials. Here we describe a computational design approach for the reversible self-assembly of proteins into tunable supramolecular fractal-like topologies in response to phosphorylation. Guided by atomic-resolution models, we develop fusions of Src homology 2 (SH2) domain or a phosphorylatable SH2-binding peptide, respectively, to two symmetric, homo-oligomeric proteins. Mixing the two designed components resulted in a variety of dendritic, hyperbranched and sponge-like topologies that are phosphorylation-dependent and self-similar over three decades (~10 nm-10 μm) of length scale, in agreement with models from multiscale computational simulations. Designed assemblies perform efficient phosphorylation-dependent capture and release of cargo proteins.

Drug research and development has a high attrition rate, with many promising drugs failing for efficacy or safety in the clinic. Increased use of detailed modeling approaches like quantitative systems pharmacology (QSP) may help in reducing overall failure rate, by helping the industry in decisions to fail early and cheaply, or to focus on patients and drug combinations that are more likely to respond or synergize, respectively. QSP offers computational methods to simulate how well different therapies may work in a patient, and therefore to better predict drug performance and reduce the cost in the development of new drug therapies. However, the development of detailed models requires a significant amount of biological data, and models often require knowledge of specific mechanisms. Coarse-grained, network-based models, such as Boolean and logic models, provide a tool for simulating complex systems without knowledge of specific mechanisms. These tools can be used to make early predictions about a biological system and can facilitate the development of more complex models. We offer a literature review of how Boolean modeling techniques are used in the identification of novel drug targets, as well as how they fall into the pipeline of developing in-depth ordinary differential equation models.