Background Growing clinical evidence shows that treatment effect of most approved combination therapies can be largely explained by the independent drug action model (IDA), i.e., the benefit a patient receives from a combination therapy is driven by the drug component his or her tumor is most sensitive to. IDA has been successfully used to predict objective response rate (ORR), progression-free-survival (PFS), and duration of response (DoR) for many combination therapies.^1-5 We built upon the model to develop a simple index to measure the added activity of a new drug when combined with any other drug to shed light on oncology drug development.

Methods When IDA is applied to ORR (aka Bliss model⁶), the ORR of the combination therapy is expected to be the sum of ORRs from Drug 1 and Drug 2 minus the product of the two (figure 1). When IDA model is applied to DoR, those who potentially respond to both drugs will have the best duration to a drug component. ORR or DoR alone is not adequate to capture the overall antitumor activity but jointly may.^7-8 Integrating results from the two, the added activity from Drug 2 to combination is:

ORRxDoR of Drug2x(1-ORRxDoR of Drug1/Sum of DoRs of Drug1 and Drug2)

Clearly, any new drug with single-agent ORR too low or DoRtoo short is unlikely to add much activity to the combination.

Results Further inspection of the index suggests that DoR of Drug 2 is more impactful than ORR. It also reveals that it is more impacted by ORR than by DoR of Drug 1, implying that there is more room for improvement over standard-of-care with lower ORR (but longer DoR) than with shorter DoR (but higher ORR). When Drug 2 has a superior DoR than Drug 1, the benefit of Drug 2 is largely retained after combination, which explains why some immune checkpoint inhibitors are successful after combining with chemotherapies. Conversely, when Drug 1 has a superior DoR than Drug 2, the benefit of Drug 2 is largely derived from those who do not respond to Drug 1, predicting the challenges in developing new drugs with short DoR for combining with established immune checkpoint inhibitors.

Conclusions We have derived a simple index to help researchers make early Go-No Go decisions on combination therapies. It is insightful, and, assisted with proper analysis method [9-10], it can potentially play a critical role in executing FDA’s forthcoming Project FrontRunner.

References

1. Palmer AC, Sorger PK. Combination cancer therapy can confer benefit via patient-to-patient variability without drug additivity or synergy. Cell 2017; 171:1678–91 e13.

2. Plana D, Palmer AC, Sorger PK. Independent drug action in combination therapy: implications for precision oncology. Cancer Discov 2022;12:606–24.

3. Schmidt E, Chisamore MJ, Chaney MF, et al. Assessment of Clinical Ctivity of PD-1 checkpoint inhibitor combination therapies reported in clinical trials. JAMA Network Open 2020; 3(2): e1920833.

4. Chen C, Liu F, Ren Y, Sun Z, Shentu Y, Schmidt EV. Independent drug action and its statistical implications for development of combination therapies. Contemporary Clinical Trials 2020; 106126.

5. Sun LZ, Wu C, Li X, Chen C, Schmidt EV. Independent Action Models and Prediction of Combination Treatment Effects for Response rate, Duration of Response and Tumor Size Change in Oncology Drug Development. Contemporary Clinical Trials 2021; 106434.

6. Bliss CI. The toxicity of poisons applied jointly. Ann. Appl. Biol 1939;26:585–615.

7. McCaw ZR, Tian L, Wei L. Appropriate analysis of duration of response data in cancer trials. JAMA Oncol 2020;6(12):1978. doi:10.1001/jamaoncol.2020.4657.

8. Chen H, Wang M, Wu C, Zhou H, Chen C, Diede S. Comparison of duration of response vs conventional response rates and progression-free survival as efficacy end points in simulated immuno-oncology clinical trials. JAMA Network Open 2021. doi:10.1001/jamanetworkopen.2021.8175.

9. Huang B, Tian L, Talukder E, Rothenberg M, Kim DH, Wei LJ. Evaluating treatment effect based on duration of response for a comparative oncology study. JAMA Oncol. 2018;4(6):874–876. doi:10.1001/jamaoncol.2018.0275.

10. Huang B, Tian L, McCaw ZR, et al. Analysis of response data for assessing treatment effects in comparative clinical studies. Ann Intern Med 2020;173(5):368–374. doi:10.7326/M20-0104.

• Download figure
• Open in new tab
• Download powerpoint

Abstract 1406 Figure 1

ORR based on the independent drug action modelThe overall area represents the patients who may respond to the combination therapy. Patients potentially responding to both drug components (overlapping area) will have longer DoR than patients only responding to one.