EMD Serono

Garadacimab, an activated factor XII (FXIIa) inhibitor monoclonal antibody, is being evaluated for the long‐term prophylaxis of hereditary angioedema. Here, we report the results from a two‐part, phase 1, open‐label, single ascending dose study assessing the pharmacokinetics (PK), pharmacodynamics, safety, and tolerability after subcutaneous (SC) and intravenous (IV) administration of garadacimab in healthy Japanese and White participants. Part 1 assessed garadacimab PK after SC administration of a 200 mg dose in weight‐matched White and Japanese participants, and 600 mg dose in Japanese participants. Part 2 assessed 3 and 10 mg/kg IV doses in Japanese participants. Follow‐up for safety was over 84 days post‐dose. Overall, 37 participants received garadacimab dosing and 36 completed the study, with one participant lost to follow‐up. Following SC administration, time to maximum plasma concentration (t max ) occurred at 7 days post‐dose, and garadacimab exposure, based on maximum plasma concentration (C max ) and area under the plasma concentration–time curve (AUC), increased less than 3‐fold when tripling the dose. PK was comparable between Japanese and White participants, with geometric mean ratios for C max and AUC close to 100%. Following IV administration, t max occurred at the end of infusion, and garadacimab exposure increased in a dose‐proportional manner. Inhibition of FXIIa‐mediated kallikrein activity versus baseline was observed in all participants receiving the SC and IV doses. No anti‐drug antibodies against garadacimab were reported. Consistent with pivotal phase 3 (VANGUARD) outcomes, no safety concerns and no difference in the safety profile of garadacimab were observed between healthy Japanese and White participants.

Objective Activation of endosomal toll‐like receptors (TLRs) is one possible driver of inflammation in idiopathic inflammatory myopathies (IIM). We investigated the potential contribution of TLR7 and TLR8 to IIM pathogenesis. Methods Activation of TLR7/8 in healthy donor peripheral blood mononuclear cells (PBMCs) by immune complexes from patients with IIMs and lupus was tested. Autoantibody profiling of patient IgG samples was performed using a 1581 antigen array. TLR7 and/or TLR8 activation by RNA molecules associated with autoantibodies was assessed. Gene expression in human myoblasts and satellite cells following treatment with supernatants from TLR7/8‐activated PBMCs was evaluated by NanoString. C57BL/6 mice were dosed intramuscularly with the TLR7/8 agonist R848 and single‐cell RNA‐sequencing was performed on the muscle to ascertain the cell types responding to TLR7/8 activation and the downstream effects. Results Overall, 69 patients with IIMs were included with representation of dermatomyositis, polymyositis, and inclusion body myositis subsets. Immune complexes from patients with IIMs, as well as autoantibody‐associated RNAs histidyl‐transfer RNA, Y1, Y4, and U1, activated PBMCs to produce interferon‐α and IL‐6 via TLR7/8. Several canonical (Ro60, Ro52, and HIST1H4A) and novel (IL‐36RN) autoreactivities correlated highly with TLR7/8 activation. Supernatants from TLR7/8‐activated PBMCs had a negative impact on human myoblasts and satellite cells. Endothelial cells were activated by R848 in mouse muscle in vivo in addition to immune cells such as monocytes and macrophages. Conclusion Our results suggest that patients with IIMs have autoantibodies in their blood causing TLR7/8 activation, which leads to inflammation in muscles with potential deleterious effects.

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.

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 (fmAO) was verified indirectly. Clearance predictions were improved by using ESFs (GMFE ≤1.4‐fold versus up to fivefold with physiologically‐based scaling only). Observed fmi from mass balance studies were crucial for model verification/refinement, as illustrated by capmatinib, where the fmAO (40%) was otherwise underpredicted up to fourfold. Subsequently, independent DDI studies with ketoconazole, itraconazole, rifampicin, and carbamazepine verified the fmCYP3A4, 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.

Physiologically‐based pharmacokinetic (PBPK) modeling offers a viable approach to predict induction drug–drug interactions (DDIs) with the potential to streamline or reduce clinical trial burden if predictions can be made with sufficient confidence. In the current work, the ability to predict the effect of rifampin, a well‐characterized strong CYP3A4 inducer, on 20 CYP3A probes with publicly available PBPK models (often developed using a workflow with optimization following a strong inhibitor DDI study to gain confidence in fraction metabolized by CYP3A4, f m,CYP3A4 , and fraction available after intestinal metabolism, Fg), was assessed. Substrates with a range of f m,CYP3A4 (0.086–1.0), Fg (0.11–1.0) and hepatic availability (0.09–0.96) were included. Predictions were most often accurate for compounds that are not P‐gp substrates or that are P‐gp substrates but that have high permeability. Case studies for three challenging DDI predictions (i.e., for eliglustat, tofacitinib, and ribociclib) are presented. Along with parameter sensitivity analysis to understand key parameters impacting DDI simulations, alternative model structures should be considered, for example, a mechanistic absorption model instead of a first‐order absorption model might be more appropriate for a P‐gp substrate with low permeability. Any mechanisms pertinent to the CYP3A substrate that rifampin might impact (e.g., induction of other enzymes or P‐gp) should be considered for inclusion in the model. PBPK modeling was shown to be an effective tool to predict induction DDIs with rifampin for CYP3A substrates with limited mechanistic complications, increasing confidence in the rifampin model. While this analysis focused on rifampin, the learnings may apply to other inducers.

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.

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.

390 Background: Avelumab 1LM is a standard of care for pts with mUC that has not progressed after 1L platinum-based chemotherapy (PBC). Immuno-oncology (IO) monotherapy is considered in pts with mUC who are ineligible to receive PBC. Recently approved 1L tx options include enfortumab vedotin + pembrolizumab and nivolumab + gemcitabine/cisplatin. This US retrospective study examined pt characteristics, tx patterns, and clinical outcomes among pts with mUC receiving 1L tx, including avelumab 1LM. Methods: Pts with mUC (bladder cancer; stage IIIB or IV, or ≥2 claims with ICD-10 diagnoses for metastatic cancer) aged ≥18 years, who initiated 1L tx (index date) from Jan 1, 2020 to Jul 31, 2023, were identified from the Healthcare Integrated Research Database (HIRD®; claims and clinical data from the health plan’s Cancer Care Quality Program). Clinical outcomes, including time to next tx (TTNT; time from 1L tx initiation to second-line tx) and overall survival (OS), were evaluated post index date. Pt characteristics and tx patterns were summarized using descriptive statistics. Avelumab 1LM use was identified on/after June 30, 2020 and ≤90 d after 1L PBC discontinuation. OS from the start of 1L tx or 1LM was analyzed using Kaplan-Meier methodology. Results: Of 2,820 pts with mUC, 1L tx was PBC in 37.0% (n=1,044), IO monotherapy in 39.0% (n=1,099), and other tx in 24.0% (n=677; antibody-drug conjugates and non-PBC). Among pts who received 1L PBC, 15.0% (n=157) initiated avelumab 1LM within 90 days of completing 1L PBC. Avelumab uptake in 2020-2023 by year was 10.8% (Jun-Dec 2020), 29.9%, 34.4%, and 24.8% (Jan-Aug 2023), respectively; 38.9% of pts were still receiving avelumab at the end of the study period. In the 1L PBC and IO cohorts, mean age (SD) was 65.5 (11.1) and 74.6 (10.7) years, respectively. 60% of pts were male. In the 1L PBC and 1L IO monotherapy cohorts, respectively, median follow-up (IQR) was 11.2 (5.6-20.3) and 8.6 (4.0-17.7) months (mo); median TTNT (IQR) was 4.8 (2.8-8.3) and 5.5 (2.8-11.0) mo; and median OS (95% CI) was 29.7 (25.1-37.2) and 20.0 (17.1-25.6) mo. In pts who received avelumab 1LM, median follow-up (IQR) from start of 1L PBC was 14.6 (9.2-21.3) mo; median TTNT (IQR) was 7.6 (6.2-12.3) mo; median time on avelumab tx was 5.0 (1.8-10.2) mo; and 1- and 2-year OS rates, respectively, from start of avelumab 1LM were 78% and 65%, and from start of 1L PBC were 84% and 68% (median OS was not estimable because >50% of pts remained alive at the end of the study period). Conclusions: Despite advances in 1L tx for mUC, IO monotherapy remains a prevailing option for older, commercially insured patients in the US. In this descriptive rw study, pts treated with 1L PBC had longer OS than pts treated with 1L IO monotherapy. Future studies with longer follow-up are needed to further evaluate pt outcomes.

393 Background: Avelumab 1LM tx is approved in the US for pts with la/mUC who do not have disease progression after platinum-based chemotherapy (PBC). The tx landscape is evolving due to the recent approvals of newer therapies for pts with la/mUC. This study examines rw tx patterns, tx sequencing post avelumab 1LM, and OS in pts with la/mUC who initiated 1L tx in the US community oncology setting. Methods: This retrospective cohort study used the US Oncology Network (iKnowMed) EHR database. The study included adult pts (≥18 years) with la/mUC initiating 1L tx (index date) between Dec 1, 2019, and Nov 30, 2023. Pts were censored for OS at their last contact date or the end of study period, Feb 28, 2024. Descriptive statistics were used to describe tx patterns. Kaplan-Meier analysis was used to evaluate OS from the start of 1LM or second-line (2L) enfortumab vedotin (EV) treatment post avelumab 1LM. Results: A total of 1,658 pts with la/mUC initiated 1L tx with a median (range) follow-up of 9.0 months (0.1-50.4). The median (range) age at diagnosis was 73 years (31-90+), 74.7% were male and 47.1% had ECOG performance status ≤1. The 1L therapies included: IO monotherapy (41.2%), PBC only (32.4%), PBC followed by avelumab 1LM (11.2%), and other tx (15.1%). Since the US FDA approval (June 30, 2020), the utilization of avelumab 1LM among 1L PBC users ranged from 25.0% to 32.9% in 2023. Median (range) follow-up from the start of avelumab 1LM was 9.1 months (0.5-42.2). During the study observation period, 44 pts (23.7%) remained on avelumab 1LM. Median (95% CI) OS from start of avelumab 1LM was 18.5 months (13.8-23.8). Among the overall study cohort, 598 (36.1%) and 196 pts (11.8%) received 2L and third-line tx, respectively. The most common 2L therapies were IO monotherapy (44.1%) and EV monotherapy (23.9%). After discontinuation of avelumab 1LM, 81 pts (43.5%) received 2L tx, 48 (59.3%) of those received EV; median OS from start of 2L EV was 12.7 months (7.2-16.5). Conclusions: These rw study findings demonstrate avelumab 1LM effectiveness for pts whose disease has not progressed on 1L PBC and provides evidence of the use of 2L EV after avelumab 1LM. There was an increased uptake of avelumab 1LM since its FDA approval. Survival outcomes are consistent with the JAVELIN Bladder 100 clinical trial and other rw studies that had shorter follow-up. We observed high IO monotherapy use despite the platinum-ineligible population typically making up 11% of pts with la/mUC. ¹ Some of these pts treated with IO monotherapy may benefit from alternative 1L regimens. Future rw studies are needed to further characterize contemporary tx patterns, optimal tx sequencing, and survival in pts with la/mUC amidst the evolving tx landscape. ¹ Gupta S, et al. JNCI 2024;116(4):547–554.

For patients with locally advanced/metastatic urothelial carcinoma (la/mUC), first-line (1L) treatment with platinum-based chemotherapy (PBC) followed by avelumab 1L maintenance (1LM) is a recommended therapy per treatment guidelines in patients without disease progression. However, contemporary real-world (rw) data among patients receiving this treatment are necessary to understand clinical outcomes and optimal treatment sequencing. This retrospective cohort study analyzed rw treatment patterns and clinical outcomes, including overall survival (rwOS) and progression-free survival (rwPFS), in patients with la/mUC receiving avelumab 1LM. From the Flatiron Health database, 214 patients who received avelumab 1LM following 1L PBC were included. From the start of avelumab 1LM, median rwOS was 23.8 months (95% CI: 18.2—not estimable [NE]) and median rwPFS was 5.1 months (95% CI: 4.1–7.0). A total of 96 patients received second-line (2L) therapy, with 53 receiving enfortumab vedotin (EV). From the start of 2L EV, median rwOS was 11.2 months (95% CI: 6.8—NE) and median rwPFS was 4.9 months (95% CI: 3.9–8.8). Treatment patterns and clinical outcomes in this study align with guidelines and outcomes observed in the JAVELIN Bladder 100 and EV-301 clinical trials and other rw studies, supporting the use of 1L PBC followed by avelumab 1LM and 2L EV for eligible patients.

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.

While A2A adenosine receptor (AR) was considered as a major contributor to adenosine-mediated immunosuppression, A2B, having the lowest affinity to adenosine, has also emerged as a potential contributor to tumor promotion. Therefore, in adenosine-rich tumor microenvironment (TME), where A2B could be complementary and/or compensatory to A2A, simultaneous targeting of A2A and A2B ARs can provide higher potential for cancer immunotherapy. We developed M1069—a highly selective dual antagonist of the A2A and A2B AR. In assays with primary human and murine immune cells, M1069 rescued IL2 production from T cells (A2A dependent) and inhibited VEGF production by myeloid cells (A2B dependent) in adenosine-high settings. M1069 also demonstrated superior suppression of the secretion of protumorigenic cytokines CXCL1, CXCL5, and rescue of IL12 secretion from adenosine-differentiated dendritic cells compared to an A2A-selective antagonist (A2Ai). In a one-way mixed lymphocyte reaction (MLR) assay, adenosine-differentiated human and murine dendritic cells treated with M1069 demonstrated superior T-cell stimulatory activity compared to dendritic cells differentiated in presence of A2Ai. In vivo, M1069 decreased tumor growth as a monotherapy and enhanced antitumor activity of bintrafusp alfa (BA) or cisplatin in syngeneic adenosinehi/CD73hi 4T1 breast tumor model, but not in the CD73 knockout 4T1 tumor model or in adenosinelow/CD73low MC38 murine colon carcinoma model. In summary, our dual A2A/A2B AR antagonist M1069 may counteract immune-suppressive mechanisms of high concentrations of adenosine in vitro and in vivo and enhance the antitumor activity of other agents, including BA and cisplatin.

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.