Learning from anti‐tau antibody clinical trials using computer simulation of spatial tau propagation

Abstract Background Based on the tau spreading hypothesis, anti‐tau antibodies have been developed that can bind to and eliminate the misfolded tau protein during the short extracellular period. However, trials with tau antibodies have come up short in improving clinical outcome in Progressive Supranuclear Palsy (PSP) or Alzheimer’s Disease (AD). It is unclear whether the negative outcomes were due to insufficient target engagement and to what extent the change of tau CSF biomarkers reflected the brain pharmacodynamics. Method A Quantitative Systems Pharmacology (QSP) computer platform of tau biology has been developed including tau species diversity and their respective concentration, neuronal activity‐dependent tau secretion, tau binding to membrane surface receptors HSPG and LRP1, tau‐antibody binding and subsequent internalization of tau at the synaptic cleft. This model is combined with a Physiology‐Based PharmacoKinetic (PBPK) model to derive brain target exposure of tau antibodies. In addition, a PBPK model is used to simulate the level of plasma and CSF biomarkers. Result The platform was calibrated with preclinical in‐vitro cellular assays and after injection of brain extract in Transgene mouse models and was further humanized using published data on seed‐competent tau from postmortem AD brain at different Braak stages. Combining the extracellular antibody exposure, the spatial dimensions and the dynamics of tau secretion, diffusion and binding to the membrane surface receptors, the model suggests that a very limited fraction (10‐20%) of misfolded tau protein can be captured by antibodies and cleared. Even less reduction (5‐10%) is predicted for Progressive Supranuclear Palsy due to the higher affinity of PSP‐4R tau for membrane surface receptors. The reductions in misfolded tau protein are greatest in earlier stages of the disease. With regard to epitope selection, we identify conditions where high specificity (i.e. monomeric vs misfolded tau) is superior to high sensitivity (affinity). Conclusion Advanced Quantitative Systems Pharmacology computer modeling combining drug exposure dynamics with mechanistic modeling allows to (1) generate testable hypotheses for the outcomes of current therapeutic interventions, (2) support improved clinical trial designs in terms of dosing, patient selection, background amyloid therapy and disease stage and (3) ultimately prioritize targets for addressing tau pathology.