A modeling informed quantitative approach to salvage clinical trials interrupted due to COVID-19

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.

Keywords: physiology‐based pharmacokinetic modeling; protocol deviations; quantitative systems pharmacology.

FIGURE 1

Detailed description of the quantitative systems pharmacology virtual twin approach. A number of patients have already completed the trial (top rows), while others who have started but not yet finished (bottom rows) will have different type of interruptions. Drug intakes (for instance biweekly antibody injections) are represented by red marks whereas X stands for a clinical visit and readout. The green stippled lines are predictions based on baseline characteristics for the actual duration of the trial for each individual patient. The patients that have not yet finished have either intermediate functional readouts or readouts after protocol amendments and trial resumption and the virtual twin predictions can be compared to these actual outcomes. The red stippled lines are then predicted outcomes from the virtual twin platform for the patients with interrupted trials and subjects that have dropped out, but according to their original uninterrupted functional trajectory. In this way the virtual twin approach can “stitch” the outcomes together and allow the real completers (patients 1.M‐1) and the virtual completers (patients M..N) to be “pooled,” allowing for an analysis by the original statistical plan