Abstract
Introduction: Rigosertib is a small molecule inhibitor of cellular signaling pathways in cancer cells by acting as a Ras mimetic. The inhibitory effect is mediated by Rigosertib binding to the Ras-binding domain found in many Ras effector proteins (Athuluri-Divakar SK, Cell 2016). Oral administration of Rigosertib was initially evaluated in lower risk MDS patients. The drug, administered as 560 mg BID (q12hr; 2/3 wks), was associated with high rates of transfusion independence but with significant GU AEs (Raza, et al, Blood 2017 130:1689). Subsequently, a reduction in the PM dose to 280 mg led to a decrease in GU AEs, suggesting a causal relationship with nocturnal bladder drug concentration. When oral administration of Rigosertib was tested with standard dose of parenteral Azacitidine in a Phase I/II trial (NCT01926587) at a dose of 560/280 mg (Q12hrs, 3/4 weeks), the ORR was 77%; with 60% for the HMA relapsed/refractory group for the high risk MDS patients. The impressive response rate, in the combination treatment, was also associated with significant GU AEs. Hence, it is important to understand the underlying cause and devise ways to maximize response rates with minimization of GU AEs.
Previously, it was established that GU AEs were unlikely to be related to the higher systemic exposure of the drug (Maniar, et al ASH 2018, Abstract submitted) but attributed to the nocturnal dwell time of high drug concentration in the bladder of patients treated with continuous oral administration (3/4 weeks). In this investigation, we developed a pharmacokinetic model to simulate the systemic and bladder exposure of Rigosertib after repeated oral dosing. The overall aim was to identify and implement an oral dosing regimen for Rigosertib that would maximize the systemic exposure with minimization of nocturnal bladder concentration, thereby potentially mitigating or eliminating the GU AE's.
Methods: A 2-compartment model with 1st order absorption and elimination (Figure 1) was fitted to data collected from patients with solid malignancies at a 560 mg dose (n=25). Model parameter estimates were then used to generate a virtual population of 100 patients. Each virtual patient was randomly assigned parameter values based on the 95% confidence interval for each parameter estimate obtained through the modeling analysis. Model simulations were then performed to evaluate the steady state systemic and urinary exposure of Rigosertib in the virtual population after treatment with different dosing schedules (Figure 2). Besides plasma exposure (Cmax, AUC), the predicted rigosertib bladder concentration during the sleep-cycle was also assessed. From this analysis, optimal dosing regimens were selected for evaluation in a Phase 2 study in HR-MDS patients in combination with Azacitidine. The model was validated by comparing the predicted systemic exposure with observed data using these optimal dosing regimens, and by comparing Grade 3 GU AE's events from the pre- and post-optimization of dosing regimen (NCT01926587).
Results: The model-predicted steady state plasma concentration-time profile of Rigosertib for different dosing regimens is shown in Figure 3. The duration of exposure of drug above minimum effective concentration (MEC, 0.175 ug/ml) was not changed by varying the dosing regimen. Importantly, model simulations demonstrated that BID dosing, with the dosing interval of 8 hours, would reduce the bladder concentration of Rigosertib by as much as 70% during sleep without compromising systemic drug exposure (Table 1). As illustrated in Table 2, model simulated Rigosertib exposure (Cmax, AUC) compared favorably with data from patients treated with the novel twice daily dosing regimens (560mg/560mg and 840 mg/280mg, dosing interval of 8 hours), thereby validating the model. Preliminary safety data from the on-going trial demonstrates that the Grade 3 GU AEs were significantly reduced (12%) with the optimized dosing regimen compared to the pre-optimized dosing regimen (29%) despite using a higher total dose of drug (1120 mg vs 840 mg).
Conclusions: This study demonstrates the utility of pharmacokinetic modeling for designing a dosing regimen directed at reducing the incidence of toxicity. The identified dosing regimen, along with mitigation strategies, successfully reduced the risk of Grade 3 GU AEs of rigosertib without compromising the duration of systemic exposure of Rigosertib above MEC in HR MDS patients.
Disclosures
Taft: Onconova Therapeutics, Inc: Research Funding. Ren:Onconova Therapeutics, Inc: Employment, Equity Ownership. Zbyszewski:Onconova Therapeutics, Inc: Employment, Equity Ownership. Morgan:Onconova Therapeutics, Inc: Consultancy. Petrone:Onconova Terapeutics Inc.: Employment, Equity Ownership. Fruchtman:Onconova Therapeutic Inc: Employment, Equity Ownership. Maniar:Onconova Therapeutics, Inc: Employment, Equity Ownership.
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