Roblitinib

The role of fibroblast growth factor receptor (FGFR) protein-tyrosine kinase inhibitors in the treatment of cancers including those of the urinary bladder

The human fibroblast growth factor family consists of 22 factors and five transmembrane receptors. Of the 22 factors, eighteen are secreted while four of them function exclusively within the cell. Four of the fibroblast growth factor receptors (FGFRs) possess intracellular protein-tyrosine kinase activity while the fifth (FGFRL1) has a short 105-residue intracellular non-enzymatic component. The FGFR protein kinase domain consists of a bi-lobed structure that is similar to that of all other protein kinases. FGFR gene alterations occur in a wide variety of cancers including those of the urinary bladder, breast, ovary, prostate, endometrium, lung, and stomach. The majority (66 %) of FGFR gene alterations involve gene amplifications, followed by mutations (26 %), and rearrangements that produce fusion proteins (8 %). Erdafitinib was the first orally effective FGFR antagonist approved by the FDA (2019) for the treatment of advanced cancer, that of the urinary bladder. FGF23 suppresses phosphate reabsorption in the proximal tubules of the kidney; FGF23 blockade allows phosphate reabsorption to occur and leads to elevated serum phosphate levels. Erdafitinib and several other, but not all, FGFR antagonists produce hyperphosphatemia. Erdafitinib binds to an inactive DGF-Din conformation of FGFR1 and is classified as a type I? inhibitor. Similarly, dovitinib, AZD4547, CH5183284, infigratinib, lenvatinib, LY2874455, and lucitanib are type I? inhibitors. The inactive conformations contain an autoinhibitory brake that is made up of three main residues: an asparagine (N) within the αC-β4 back loop, a glutamate (E) corresponding to the second hinge residue, and a lysine (K) in the β8-strand (the NEK triad). PDGFRα/β, Kit, CSF1R, VEGFR1/2/3, Flt3, Tek, and Tie protein kinases are also regulated by a similar autoinhibitory brake mechanism. Ponatinib binds to FGFR4 in a DFG-Dout conformation and is classified as a type II inhibitor. Futibatinib, roblitinib, H3B-6527, fisogatinib, and PRN1371 bind covalently to their FGFR target and are classified as type VI inhibitors. Nintedanib, pazopanib, pemigatinib, rogaratinib, fisogatinib, and PRN1371 are FGFR inhibitors lacking drug-enzyme crystal structures. All of the aforementioned FGFR antagonists are orally effective. The development of FGFR inhibitors has lagged behind those of other receptor protein-tyrosine kinases. However, the FDA approval of erdafitinib for the treatment of urinary bladder cancers may stimulate additional work targeting the many other FGFR-driven neoplasms.

Discovery of Roblitinib (FGF401) as a Reversible-Covalent Inhibitor of the Kinase Activity of Fibroblast Growth Factor Receptor 4

FGF19 signaling through the FGFR4/β-klotho receptor complex has been shown to be a key driver of growth and survival in a subset of hepatocellular carcinomas, making selective FGFR4 inhibition an attractive treatment opportunity. A kinome-wide sequence alignment highlighted a poorly conserved cysteine residue within the FGFR4 ATP-binding site at position 552, two positions beyond the gate-keeper residue. Several strategies for targeting this cysteine to identify FGFR4 selective inhibitor starting points are summarized which made use of both rational and unbiased screening approaches. The optimization of a 2-formylquinoline amide hit series is described in which the aldehyde makes a hemithioacetal reversible-covalent interaction with cysteine 552. Key challenges addressed during the optimization are improving the FGFR4 potency, metabolic stability, and solubility leading ultimately to the highly selective first-in-class clinical candidate roblitinib.

Contribution of machine learning to tumor growth inhibition modeling for hepatocellular carcinoma patients under Roblitinib (FGF401) drug treatment

Machine learning (ML) opens new perspectives in identifying predictive factors of efficacy among a large number of patients' characteristics in oncology studies. The objective of this work was to combine ML with population pharmacokinetic/pharmacodynamic (PK/PD) modeling of tumor growth inhibition to understand the sources of variability between patients and therefore improve model predictions to support drug development decisions. Data from 127 patients with hepatocellular carcinoma enrolled in a phase I/II study evaluating once-daily oral doses of the fibroblast growth factor receptor FGFR4 kinase inhibitor, Roblitinib (FGF401), were used. Roblitinib PKs was best described by a two-compartment model with a delayed zero-order absorption and linear elimination. Clinical efficacy using the longitudinal sum of the longest lesion diameter data was described with a population PK/PD model of tumor growth inhibition including resistance to treatment. ML, applying elastic net modeling of time to progression data, was associated with cross-validation, and allowed to derive a composite predictive risk score from a set of 75 patients' baseline characteristics. The two approaches were combined by testing the inclusion of the continuous risk score as a covariate on PD model parameters. The score was found as a significant covariate on the resistance parameter and resulted in 19% reduction of its variability, and 32% variability reduction on the average dose for stasis. The final PK/PD model was used to simulate effect of patients' characteristics on tumor growth inhibition profiles. The proposed methodology can be used to support drug development decisions, especially when large interpatient variability is observed.

Integration of Pharmacokinetics, Pharmacodynamics, Safety, and Efficacy into Model-Informed Dose Selection in Oncology First-in-Human Study: A Case of Roblitinib (FGF401)

Model-informed dose selection has been drawing increasing interest in oncology early clinical development. The current paper describes the example of FGF401, a selective fibroblast growth factor receptor 4 (FGFR4) inhibitor, in which a comprehensive modeling and simulation (M&S) framework, using both pharmacometrics and statistical methods, was established during its first-in-human clinical development using the totality of pharmacokinetics (PK), pharmacodynamic (PD) biomarkers, and safety and efficacy data in patients with cancer. These M&S results were used to inform FGF401 dose selection for future development. A two-compartment population PK (PopPK) model with a delayed 0-order absorption and linear elimination adequately described FGF401 PK. Indirect PopPK/PD models including a precursor compartment were independently established for two biomarkers: circulating FGF19 and 7α-hydroxy-4-cholesten-3-one (C4). Model simulations indicated a close-to-maximal PD effect achieved at the clinical exposure range. Time-to-progression was analyzed by Kaplan-Meier method which favored a trough concentration (C_trough )-driven efficacy requiring C_trough above a threshold close to the drug concentration producing 90% inhibition of phospho-FGFR4. Clinical tumor growth inhibition was described by a PopPK/PD model that reproduced the dose-dependent effect on tumor growth. Exposure-safety analyses on the expected on-target adverse events, including elevation of aspartate aminotransferase and diarrhea, indicated a lack of clinically relevant relationship with FGF401 exposure. Simulations from an indirect PopPK/PD model established for alanine aminotransferase, including a chain of three precursor compartments, further supported that maximal target inhibition was achieved and there was a lack of safety-exposure relationship. This M&S framework supported a dose selection of 120 mg once daily fasted or with a low-fat meal and provides a practical example that might be applied broadly in oncology early clinical development.

A first-in-human phase 1/2 study of FGF401 and combination of FGF401 with spartalizumab in patients with hepatocellular carcinoma or biomarker-selected solid tumors

Background: Deregulation of FGF19-FGFR4 signaling is found in several cancers, including hepatocellular carcinoma (HCC), nominating it for therapeutic targeting. FGF401 is a potent, selective FGFR4 inhibitor with antitumor activity in preclinical models. This study was designed to determine the recommended phase 2 dose (RP2D), characterize PK/PD, and evaluate the safety and efficacy of FGF401 alone and combined with the anti-PD-1 antibody, spartalizumab.
Methods: Patients with HCC or other FGFR4/KLB expressing tumors were enrolled. Dose-escalation was guided by a Bayesian model. Phase 2 dose-expansion enrolled patients with HCC from Asian countries (group1), non-Asian countries (group2), and patients with other solid tumors expressing FGFR4 and KLB (group3). FGF401 and spartalizumab combination was evaluated in patients with HCC.
Results: Seventy-four patients were treated in the phase I with single-agent FGF401 at 50 to 150 mg. FGF401 displayed favorable PK characteristics and no food effect when dosed with low-fat meals. The RP2D was established as 120 mg qd. Six of 70 patients experienced grade 3 dose-limiting toxicities: increase in transaminases (n = 4) or blood bilirubin (n = 2). In phase 2, 30 patients in group 1, 36 in group 2, and 20 in group 3 received FGF401. In total, 8 patients experienced objective responses (1 CR, 7 PR; 4 each in phase I and phase II, respectively). Frequent adverse events (AEs) were diarrhea (73.8%), increased AST (47.5%), and ALT (43.8%). Increase in levels of C4, total bile acid, and circulating FGF19, confirmed effective FGFR4 inhibition. Twelve patients received FGF401 plus spartalizumab. RP2D was established as FGF401 120 mg qd and spartalizumab 300 mg Q3W; 2 patients reported PR.
Conclusions: At biologically active doses, FGF401 alone or combined with spartalizumab was safe in patients with FGFR4/KLB-positive tumors including HCC. Preliminary clinical efficacy was observed. Further clinical evaluation of FGF401 using a refined biomarker strategy is warranted.
Trial registration: NCT02325739 .