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EP36104
Poster Title: Monoclonal antibody therapy efficacy can be boosted by combinations with other treatments: predictions from integrated Alzheimer’s disease QSP platform
Submitted on 07 Apr 2021
Author(s): Tatiana Karelina, Stepan Lerner
Affiliations: InSysBio LLC, Moscow
This poster was presented at ACoP11
Poster Views: 1,213
Submitted on 07 Apr 2021
Author(s): Tatiana Karelina, Stepan Lerner
Affiliations: InSysBio LLC, Moscow
This poster was presented at ACoP11
Poster Views: 1,213
Abstract: Objectives: Clearance of pathological accumulations of amyloid beta (Aβ) and tau protein are still considered as important requirement for the treatment of Alzheimer’s disease. It is known that pathologies interact, but the efficacy of amyloid targeting therapies on tau pathology is questionable and pharmacodynamics of CSF tau markers differ from amyloid markers. Improvement of efficacy could be achieved by combinations of treatments. Quantitative systems pharmacology model allows for integration of available information from multiple preclinical studies to simulate clinical studies. The goal was to apply the translational systems pharmacology model of key AD markers to simulate efficacy of potential combinations of known treatments with perspective immunotherapy.
Methods: Aβ [1] and tau pathology submodels are merged through the model of intracellular protein degradation systems (proteasome, autophagy) which govern protein accumulation and can be disrupted progressively in AD. Literature data (concentration baselines and dynamics, SUVR) from mice and human, in vitro perturbations of cellular pathways (rapamycin, proteasome inhibitors, vinblastine, ACAT inhibitors, calpain inhibitors) have been used for calibration. Simplified pharmacokinetic model of antibodies describes distribution to the brain and target binding, preventing seeding and growth of aggregates; it was calibrated on the data for non-specific IgG [2], and partially verified of BAN2401 CSF data. Data from clinical studies for aducanumab and BACE inhibitor (verubecestat) have been used for model validation, together with mouse data for treatment by rapamycin, calpain inhibitors, proteasome activation as well as tau targeting antibodies, DC8E8 (tau) [3] and GW-23B7 (anti-β-sheet), in Tau and APP/Tau mouse AD models.
Result: Model correctly describes progression and interaction the Aβ and tau pathologies. It correctly predicts effect of rapamycin, calpain inhibitors, proteasome activation on tau or amyloid in transgenic mouse models, as well as efficacy of antibodies. Predictions for aducanumab efficacy in Tg2576 mouse [4]
correspond to the data (about 40-50% for soluble and insoluble amyloid). Translation to clinical trial predicts correctly amyloid SUVR decrease by 0.2 from baseline within 54 weeks [4]. Tau in cortex and hippocampus is predicted to change by 10-15%. Similar prediction was derived for verubecestat low efficacy towards tau in CSF. Simulated addition of rapamycin to aducanumab therapy increase efficacy tremendously, leading to 80% brain tau reduction within one year and acceleration of amyloid clearance.
Conclusion: QSP modeling allows for choice of combinations of newly emerging biologics combined with targeting cell metabolism, to lead optimization and increase of treatment efficacy. Summary: QSP modeling allows for choice of combinations of newly emerging biologics combined with targeting cell metabolism, to lead optimization and increase of treatment efficacy. References: 1. Karelina, T. et al. CPT: Pharmacometrics & Systems Pharmacology (2017) 6(10), 676– 685.
2. Chang, H.-Y. (2019). Journal of Pharmacokinetics and Pharmacodynamics (2019) 46(4), 319–338
3. Kontsekova, E. et al. Alzheimers Res Ther (2014) 6(4), 45.
4. Sevigny, J. The antibody aducanumab reduces Aβ plaques in Alzheimer’s disease. Nature, (2016). 537(7618), 50–56.
Methods: Aβ [1] and tau pathology submodels are merged through the model of intracellular protein degradation systems (proteasome, autophagy) which govern protein accumulation and can be disrupted progressively in AD. Literature data (concentration baselines and dynamics, SUVR) from mice and human, in vitro perturbations of cellular pathways (rapamycin, proteasome inhibitors, vinblastine, ACAT inhibitors, calpain inhibitors) have been used for calibration. Simplified pharmacokinetic model of antibodies describes distribution to the brain and target binding, preventing seeding and growth of aggregates; it was calibrated on the data for non-specific IgG [2], and partially verified of BAN2401 CSF data. Data from clinical studies for aducanumab and BACE inhibitor (verubecestat) have been used for model validation, together with mouse data for treatment by rapamycin, calpain inhibitors, proteasome activation as well as tau targeting antibodies, DC8E8 (tau) [3] and GW-23B7 (anti-β-sheet), in Tau and APP/Tau mouse AD models.
Result: Model correctly describes progression and interaction the Aβ and tau pathologies. It correctly predicts effect of rapamycin, calpain inhibitors, proteasome activation on tau or amyloid in transgenic mouse models, as well as efficacy of antibodies. Predictions for aducanumab efficacy in Tg2576 mouse [4]
correspond to the data (about 40-50% for soluble and insoluble amyloid). Translation to clinical trial predicts correctly amyloid SUVR decrease by 0.2 from baseline within 54 weeks [4]. Tau in cortex and hippocampus is predicted to change by 10-15%. Similar prediction was derived for verubecestat low efficacy towards tau in CSF. Simulated addition of rapamycin to aducanumab therapy increase efficacy tremendously, leading to 80% brain tau reduction within one year and acceleration of amyloid clearance.
Conclusion: QSP modeling allows for choice of combinations of newly emerging biologics combined with targeting cell metabolism, to lead optimization and increase of treatment efficacy. Summary: QSP modeling allows for choice of combinations of newly emerging biologics combined with targeting cell metabolism, to lead optimization and increase of treatment efficacy. References: 1. Karelina, T. et al. CPT: Pharmacometrics & Systems Pharmacology (2017) 6(10), 676– 685.
2. Chang, H.-Y. (2019). Journal of Pharmacokinetics and Pharmacodynamics (2019) 46(4), 319–338
3. Kontsekova, E. et al. Alzheimers Res Ther (2014) 6(4), 45.
4. Sevigny, J. The antibody aducanumab reduces Aβ plaques in Alzheimer’s disease. Nature, (2016). 537(7618), 50–56.
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