Dataset Information

A systems pharmacology model for gene therapy in sickle cell disease.

ABSTRACT: We developed a mathematical model for autologous stem cell therapy to cure sickle cell disease (SCD). Experimental therapies using this approach seek to engraft stem cells containing a curative gene. These stem cells are expected to produce a lifelong supply of red blood cells (RBCs) containing an anti-sickling hemoglobin. This complex, multistep treatment is expensive, and there is limited patient data available from early clinical trials. Our objective was to quantify the impact of treatment parameters, such as initial stem cell dose, efficiency of lentiviral transduction, and degree of bone marrow preconditioning on engraftment efficiency, peripheral RBC numbers, and anti-sickling hemoglobin levels over time. We used ordinary differential equations to model RBC production from progenitor cells in the bone marrow, and hemoglobin assembly from its constituent globin monomers. The model recapitulates observed RBC and hemoglobin levels in healthy and SCD phenotypes. Treatment simulations predict dynamics of stem cell engraftment and RBC containing the therapeutic gene product. Post-treatment dynamics show an early phase of reconstitution due to short lived stem cells, followed by a sustained RBC production from stable engraftment of long-term stem cells. This biphasic behavior was previously reported in the literature. Sensitivity analysis of the model quantified relationships between treatment parameters and efficacy. The initial dose of transduced stem cells, and the intensity of myeloablative bone marrow preconditioning are predicted to most positively impact long-term outcomes. The quantitative systems pharmacology approach used here demonstrates the value of model-assisted therapeutic design for gene therapies in SCD.

SUBMITTER: Zheng B

PROVIDER: S-EPMC8302248 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Sickle cell disease (SCD) is a group of inherited genetic conditions associated with lifelong complications and increased healthcare resource utilization. Standard treatment for SCD in the US varies based on stage of the disease and observed clinical severity. In this study, we aim to evaluate the potential cost-effectiveness of a durable cell or gene therapy cure for sickle cell disease from the US healthcare sector perspective. We developed a lifetime Markov model to evaluate the cost-effectiveness of a hypothetical single-administration durable treatment (DT) for SCD provided at birth, relative to standard of care (SOC). We informed model inputs including direct healthcare costs, health state utility weights, transition probabilities, and mortality rates using a retrospective database analysis of commercially insured individuals and the medical literature. Our primary outcome of interest was the incremental cost-effectiveness ratio (ICER) of DT versus SOC evaluated at a base case willingness-to-pay (WTP) threshold of $150,000 per quality-adjusted life year (QALY). We tested the robustness of our base case findings through scenario, deterministic sensitivity (DSA), and probabilistic sensitivity analyses (PSA). In the base case analysis, treatment with DT was cost-effective with an ICER of $140,877/QALY relative to SOC for a hypothetical cohort involving 47% females. Both males (ICER of $135,574/QALY) and females (ICER of $146,511/QALY) were similarly cost-effective to treat. In univariate DSA the base case ICER was most sensitive to the costs of treating males, DT treatment cost, and the discount rate. In PSA, DT was cost-effective in 32.7%, 66.0%, and 92.6% of 10,000 simulations at WTP values of $100,000, $150,000, and $200,000 per QALY, respectively. A scenario analysis showed cost-effectiveness of DT is highly contingent on assumed lifetime durability of the cure. A hypothetical cell or gene therapy cure for SCD is likely to be cost-effective from the US healthcare sector perspective. Large upfront costs of a single administration cure are offset by significant downstream gains in health for patients treated early in life. We find cost-effectiveness outcomes do not vary substantially by gender; however, several model parameters including assumed durability and upfront cost of DT are likely to influence cost-effectiveness findings.

Dataset Information

A systems pharmacology model for gene therapy in sickle cell disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets