ABSTRACT
Abstract Background The COVID-19 pandemic has halted many Alzheimer?s Disease clinical trials, with some forced to re-start at different points along the trial time-line with substantial protocol amendments and patients dropping out with substantial partial assessments. To align the functional outcomes of these protocol amendments with the original trial design at the individual patient level, we propose to use Physiology-Based Pharmacokinetic (PBPK) and Quantitative Systems Pharmacology (QSP) Modeling to ?correct? the cognitive trajectory of mirror virtual patient population back to the original trail design. Method The Virtual Twin approach creates a PBPK computer-simulated model of each patient with a virtual twin QSP model of trial subjects, with the same co-medications, common genotype variants affecting metabolism and cognitive outcome;?-amyloid and tau biomarkers. The QSP platform is a previously ADAS-Cog calibrated model of key neuronal circuits involved in cognition, allowing to model the effects of CNS active co-medications based on their pharmacology and genotypes based on imaging studies. In this Virtual Twin approach, the platform will be extensively validated against the actual clinical data from the completer set and the fragmented outcomes of the restarters with their individual protocol amendments, before simulating the cognitive trajectory with the original trial design for those whose trial was interrupted. Result Different interruption scenarios in a 24-month AD study of a bi-weekly amyloid antibody infusion are simulated with increased anxiolytics and anti-depressants use after restart. The impact of changes is dependent upon the genotype combination with the 5-HTTLPR rs 23351 > APOE > COMTVal158Met and different for placebo and active treatment. Anti-depressants and benzo also differentially substantially affect the cognitive trajectory. We illustrate how to reconstruct the cognitive trajectory of 600 subjects (out of 1200) affected by the interruption. Conclusion Combining QSP modeling of the biology with PBPK modeling and extensive validation with the fragmented clinical data available, in principle allows to reconstruct the original cognitive trajectory in patients affected by the COVID-19 interruption. In this way, the original trial design and statistical analysis plan can be applied to achieve a fair evaluation of the clinical effect of the investigative new drug.
ABSTRACT
Many ongoing Alzheimer's disease central nervous system clinical trials are being disrupted and halted due to the COVID-19 pandemic. They are often of a long duration' are very complex; and involve many stakeholders, not only the scientists and regulators but also the patients and their family members. It is mandatory for us as a community to explore all possibilities to avoid losing all the knowledge we have gained from these ongoing trials. Some of these trials will need to completely restart, but a substantial number can restart after a hiatus with the proper protocol amendments. To salvage the information gathered so far, we need out-of-the-box thinking for addressing these missingness problems and to combine information from the completers with those subjects undergoing complex protocols deviations and amendments after restart in a rational, scientific way. Physiology-based pharmacokinetic (PBPK) modeling has been a cornerstone of model-informed drug development with regard to drug exposure at the site of action, taking into account individual patient characteristics. Quantitative systems pharmacology (QSP), based on biology-informed and mechanistic modeling of the interaction between a drug and neuronal circuits, is an emerging technology to simulate the pharmacodynamic effects of a drug in combination with patient-specific comedications, genotypes, and disease states on functional clinical scales. We propose to combine these two approaches into the concept of computer modeling-based virtual twin patients as a possible solution to harmonize the readouts from these complex clinical datasets in a biologically and therapeutically relevant way.