Integrating antibody–drug conjugate bioanalytical measures using PK–PD modeling & simulation Review
clinical PK–PD relationship of brentuximab-vedotin was a systems model, it was amicable for translating the ‘ADC dose versus unconjugated drug tumor expo- sure’ relationship to the clinic. Hence, the PK–PD model was subsequently employed for preclinical- to-clinical translation of ADC efficacy, and a priori predication of the clinical response of brentuximab- vedotin. While translating the PK–PD model to the clinic, systems parameter for the model (e.g., tumor volume and tumor growth rate) were changed to match clinical values. One of the most important parameter for the model, which is also one of the most challenging parameter to obtain bioanalytically, is the targeted receptor number per cancer cells. In case of brentuximab-vedotin that parameter was fortunately available, and hence updated during the translation of the model. Of note, the number of CD30 receptors on human cancer cells were found to be five-times less than the receptors numbers found on cancer cells from the animal models. During the preclinical-to-clinical translation of PK–PD model it was also assumed that the efficacy and potency of the unconjugated drug in the tumor is the same between preclinical and clinical cancer cells. The translated PK–PD model was able to a priori predict progression-free survival and complete response rates of brentuximab-vedotin in the clinic very well [8]. The mechanism-based PK–PD model for ADCs can
also be a very valuable tool for improving the discov- ery and development of ADCs. Figure 7 showcases this ability of the model by using 3D simulations performed using the brentuximab-vedotin PK–PD model under different scenarios. Figure 7A addresses a commonly asked precision medicine related question that what is the cut-off value for number of receptor per cell that should be employed to select the patients for a clini- cal trial, to make sure that all the patients have clini- cally meaningful efficacious response at the clinically approved dose. The relationship generated in Figure 7A can help find out an answer to this question. However, one needs to move on from the arbitrary immunohis- tochemistry scaling system to absolute quantitation of receptor number per cells to utilize the relationship. At the ADC discovery stage it is often required to know the optimum affinity of mAb toward antigen, increasing which will result in diminishing return on investment. Figure 7B addresses this issue by providing the relationship between ADC dose, mAb affinity and changes in clinical tumor volumes. Figure 7C addresses an important issue of the efflux rate of unconjugated drug out of the cell. This parameter is not only impor- tant for discovering a desirable drug molecule, but it is also important for understanding the development of drug resistance to ADCs. As shown in Figure 7C,
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the efficacy of ADC is very sensitive to changes in this parameter, and even a moderate increase in the efflux rate of a drug can render the ADC ineffective at oth- erwise efficacious doses. While most of the patient selection criteria for ADC testing in the clinic does not involve tumor growth rate, the PK–PD model sheds some light on the importance of tumor growth rate in achieving the ADC efficacy. As shown in Figure 7D & E, both the exponential and linear growth rates of the tumor are inversely proportion to ADC efficacy. How- ever, ADC efficacy is very sensitive to the changes in the exponential growth rate of the tumor, which occurs usually when the tumor is below the detection limit of currently available technology (e.g., positron emission tomography imaging). Thus, finding out better meth- ods that can detect even smaller masses of cancer cells can be of a great benefit for precision medicine efforts. Lastly, the model can also provide help with the devel- opment of back-up ADC programs. As
shown in
Figure 7F, once the clinical maximum tolerated dose of a lead ADC is known, the model can help you decide if it is worthwhile to pursue a given target with cer- tain antigen expression by improving the affinity of the mAb toward the antigen. In sum, PK–PD models are capable of influencing an ADC program during dis- covery, translation and dosing regimen optimization stage in the clinic.
Integration of toxicodynamic data Toxicity of ADCs is probably the least established and quantitatively challenging aspect of ADCs. While it is desirable to establish a reliable and continuous quan- titative relationship between ADC exposure and tox- icity using a PK–toxicodynamic (TD) model, which analyte in plasma/tissues correlates best with ADC toxicity is still not known. Additionally, many tox- icities like peripheral neuropathy are very subjective and categorical in nature, making the development of PK–TD models even more difficult. Nonetheless, tox- icities pertaining to blood cells like neutropenia and thrombocytopenia are still amicable for the develop- ment of PK–TD model. Consequently, several PK–TD models have been developed for ADCs based on the TD model developed by Friberg et al. for chemo- therapeutic drugs [27]. A generic structure of the TD model is shown in Figure 8A. In the model, the ADC is assumed to act on the stem cells and progenitor cells that produce blood cells (e.g., neutrophils and plate- lets), by either inhibiting the proliferation of those cells or accelerating the elimination of the cells. As a result of which the circulating numbers of observed blood cells decreases, leading to the toxic effects of ADCs. Tatipalli et al. [28] have used the model described in Figure 8A for characterizing ADC induced neutropenia
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