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Cellular and Gene Therapies: What Is My Optimal Dose?

One of the basic tenets of pharmaceutical drug development is the selection of an optimal dose to give to patients. Dose selection is important because too small of a dose may compromise efficacy and too large of a dose may compromise safety.

Dose selection can be challenging for any drug program, but how do you pick the right dose for nontraditional drugs like cellular therapy or gene therapy?  The answer is to leverage the power of pharmacometrics (e.g., modeling and simulation and population pharmacokinetics and pharmacodynamics).

The Intersection of Pharmacokinetics and Pharmacodynamics with Dose

For small and large molecule drugs, dose selection and dose justification center around pharmacokinetics and pharmacodynamics (PK/PD).

Pharmacokinetics (PK) provides insights into drug exposure (e.g., Cmax and AUC) and estimates of the drug’s half-life following dosing. For small and large molecule drugs, a drug’s half-life can be an important driver for determining the optimal dosage. For example, to maintain optimal therapeutic concentrations, a drug with a half-life of around 24 hours (give or take several hours) could be administered once daily, while a drug with a 12-hour half-life might be given once in the morning and once at night.

However, in order to conduct PK analysis, one must be able to measure the molecule of interest in a biological matrix such as blood, plasma, or urine following administration, which can be challenging for cellular or gene therapies. For cellular or gene therapies, an alternative method that can be used for dose selection focuses on the therapy’s effects in the body or how the body responds following administration.

Pharmacodynamics (PD) refers to the effect(s) of a drug on the body and, like PK, can support dose selection and dose justification. For example, a small molecule that affects the coagulation cascade in blood will have a PD effect on either coagulation or anticoagulation. The PK characteristics of an anticoagulant are determined by measuring the concentration of the drug in plasma over time, but the pharmacodynamic effect is determined by measuring various blood coagulation parameters over time. In this case, dose selection would attempt to optimize a safe anticoagulant effect, enough to prevent excess clots, but not so much that dangerous bleeding occurs.

Pharmacodynamics can also support dose selection for cellular and gene therapies. Knowing what the PD effects are and how to measure them in patients is key, just as it is in the small molecule anticoagulant scenario above. For example, the PD response following administration of allogeneic islet cells to a patient with type 1 diabetes could be assessed by evaluating glucose control and hemoglobin A1c levels following islet transplantation. In this case, an optimal dose may be chosen based upon the relationship between the number of islets infused and these response parameters.

Cellular Kinetics for Dose Selection

Cellular kinetics is similar to traditional pharmacokinetics but is used to describe the interaction of a cellular therapy/gene therapy with the body.  While small and large molecule drugs decay following administration, cellular/gene therapies may expand or proliferate in the body following administration.

For example, Chimeric Antigen Receptor T-Cells (CAR-T) are autologous T cells that are genetically modified ex-vivo with an antigen receptor.  The CAR-T cells are returned to the same donor and these modified T-cells proliferate in the body to provide an immune response for malignant cells expressing a certain receptor.  These therapies typically have novel transgenes that are distinct from the patients’ own genetic disposition and can be measured using techniques such as quantitative PCR (qPCR).  As such, the transgene is the primary molecule being measured and used to describe the cellular kinetics of CAR-T cells.

Using a unique transgene as the analyte, cellular kinetics can describe the amount of cells infused and the effect of extrinsic and intrinsic factors (race, sex, weight, age, etc.) on the expansion of modified T cells in vivo. Also, by monitoring the unique transgene, any negative or positive effects on the co-administration of chemotherapeutic agents on cellular expansion can be assessed, exposure-response and dose-response analyses for key adverse events and efficacy parameters (e.g., partial or complete response/remission or duration of remission) can be evaluated, and the impact of immunogenicity on CAR-T cells can be determined.

Interestingly, the cellular kinetics of CAR-T CTL019 (tisagenlecleucel; KYMRIAH™) have a time course that would be familiar to any Pharmacokineticist (Stein 2017; Stein 2019).  Dose selection and dose justification can be based on evaluating cellular kinetics in combination with measurable pharmacodynamic responses using modeling and simulation (i.e., pharmacometric) techniques that sample a population of individuals participating in clinical studies.

Pharmacometrics (Modeling and Simulation) Primer

Pharmacometrics (including population PK/PD modeling and simulation) uses models of drug concentration and/or effect, often coupled with simulations based upon the chosen model, to inform drug development decisions like dose selection and dose justification. Collectively forming the backbone of Model Informed Drug Development (MIDD), these methods fill a critical role in efficiently moving drugs, whether traditional or nontraditional, from early development through clinical trials, approval, and beyond.

As a nod to the importance of these methods, regulatory authorities like the FDA and EMA have created cross-disciplinary working groups to support them, and modeling and simulation were among the key highlights of a speech in September 2017 from then FDA Commissioner Scott Gottlieb about ways to streamline drug development.

Pharmacometric models are, in simple terms, a series of mathematical equations that describe the behavior of a drug. These models are built based upon an understanding of the characteristics of the drug as collected in nonclinical and clinical studies.  As more data are gathered, the understanding of the drug/therapy is refined and the models are updated to best approximate observed data.

Leveraging Pharmacometrics to Select and Justify Doses for Cellular Therapy and Gene Therapy

Dose selection and justification start with proper planning from the beginning of a development program to build a story of how your drug/therapy interacts with the body and how the body responds in kind.  As details of the underlying mechanism of action emerge and IND-enabling studies are planned, consideration should be given during nonclinical development for the use of modeling and simulation to inform dose selection for human clinical studies.  Pharmacometric models describe the current understanding of the drug/therapy and simulations help predict next steps for testing the drug in animals and humans.

Dose selection and dose justification of a cellular therapy or gene therapy could be determined along the following path:

  • Identify or predict analytes that can be measured after drug administration and can be analyzed pharmacokinetically or using cellular kinetics
  • Determine if any biomarkers or pharmacodynamic effects are known and can be measured for the drug/therapy
  • Correlate drug concentration to effect, if possible, in animals and scale such correlations to humans using simulations based on a mathematical model of the drug’s behavior.
  • Predict anticipated Phase 1 study human doses from animal studies using modeling and simulations
  • Test a range of human doses in Phase 1 and refine the mathematical model based on those investigations that could include PK, PD, and cellular kinetic data.
  • Use the updated model that has been built with animal and human data to simulate and predict a dose range to investigate in Phase 2 clinical studies.
  • Refine the Pharmacometric model with Phase 2 clinical data and further inform dose selection for confirmatory Phase 3 clinical studies
  • Use Phase 3 data to solidify the overall understanding of the drug’s PK/PD or cellular kinetics in the target patient population and fill in any gaps needed to support a regulatory filing for marketing approval (NDA, BLA).

Based upon an understanding of the underlying biology and data generated from nonclinical and clinical studies, pharmacometric models can be built, refined, and applied to make predictions. These predictions can support not only product development decisions like appropriate dosages for clinical trials, but can also provide key support to the product labeling and, most critically, to the safe and effective use of the drug once on the market.

Conclusions

When developing any therapeutic, including cellular and gene therapies, one needs to consider the question, “What is my optimal dose?” Modeling and simulation should be considered to support optimal dosing throughout drug development using nonclinical and clinical data and to justify the selected dose to regulatory agencies for marketing approval.

As more and more nontraditional therapies enter the clinic, pharmacometrics will play an ever-increasing role in dose selection and justification. Being able to leverage these tools effectively can make for more efficient drug development and help get promising therapies to patients sooner.

If you are developing a cellular or gene therapy, don’t wait until phase 2 or 3 clinical studies to start understanding the exposure-response relationship(s) and justifying your dose. Contact us today to benefit from Nuventra’s extensive experience with developing models for small molecules, large molecules, cellular therapies, and gene therapies.


References

Stein AM, Grupp SA, Levine JE, Laetsch LW, Pulsipher MA, Boyer MW, August KJ, Levine BL, June CH, Tomassian L, Shah S, Leung M, Huang PH, Jolivet S, Awasthi R, Mueller KT, Wood PA, Maude SL.  CTL019 Model-Based Cellular Kinetic Analysis of Chimeric Antigen Receptor (CAR) T Cells to Characterize the Impact of Tocilizumab on Expansion and to Identify Correlates of Cytokine Release Syndrome Severity. Blood 2017 130:2561

Stein AM, Grupp SA, Levine JE, Laetsch TW, Pulsipher MA, Boyer MW, August KJ, Levine BL, Tomassian L, Shah S, Leung M, Huang P-H, Awasthi R, Mueller KT, Wood PA, June CH. Tisagenlecleucel Model‐Based Cellular Kinetic Analysis of Chimeric Antigen Receptor–T Cells. CPT Pharmacometrics Syst Pharmacol. 2019 Mar 7. doi: 10.1002/psp4.12388

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