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Alzheimers Res Ther. 2018 Feb 2;10(1):14. doi: 10.1186/s13195-018-0343-5.

Impact of amyloid-beta changes on cognitive outcomes in Alzheimer's disease: analysis of clinical trials using a quantitative systems pharmacology model.

Author information

1
In Silico Biosciences, 686 Westwind Dr, Berwyn, PA, 1312, USA. Hugo-Geerts@In-Silico-Biosciences.com.
2
Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Hugo-Geerts@In-Silico-Biosciences.com.
3
In Silico Biosciences, 686 Westwind Dr, Berwyn, PA, 1312, USA.
4
Amazon AI AWS, Portland, OR, USA.

Abstract

BACKGROUND:

Despite a tremendous amount of information on the role of amyloid in Alzheimer's disease (AD), almost all clinical trials testing this hypothesis have failed to generate clinically relevant cognitive effects.

METHODS:

We present an advanced mechanism-based and biophysically realistic quantitative systems pharmacology computer model of an Alzheimer-type neuronal cortical network that has been calibrated with Alzheimer Disease Assessment Scale, cognitive subscale (ADAS-Cog) readouts from historical clinical trials and simulated the differential impact of amyloid-beta (Aβ40 and Aβ42) oligomers on glutamate and nicotinic neurotransmission.

RESULTS:

Preclinical data suggest a beneficial effect of shorter Aβ forms within a limited dose range. Such a beneficial effect of Aβ40 on glutamate neurotransmission in human patients is absolutely necessary to reproduce clinical data on the ADAS-Cog in minimal cognitive impairment (MCI) patients with and without amyloid load, the effect of APOE genotype effect on the slope of the cognitive trajectory over time in placebo AD patients and higher sensitivity to cholinergic manipulation with scopolamine associated with higher Aβ in MCI subjects. We further derive a relationship between units of Aβ load in our model and the standard uptake value ratio from amyloid imaging. When introducing the documented clinical pharmacodynamic effects on Aβ levels for various amyloid-related clinical interventions in patients with low Aβ baseline, the platform predicts an overall significant worsening for passive vaccination with solanezumab, beta-secretase inhibitor verubecestat and gamma-secretase inhibitor semagacestat. In contrast, all three interventions improved cognition in subjects with moderate to high baseline Aβ levels, with verubecestat anticipated to have the greatest effect (around ADAS-Cog value 1.5 points), solanezumab the lowest (0.8 ADAS-Cog value points) and semagacestat in between. This could explain the success of many amyloid interventions in transgene animals with an artificial high level of Aβ, but not in AD patients with a large variability of amyloid loads.

CONCLUSIONS:

If these predictions are confirmed in post-hoc analyses of failed clinical amyloid-modulating trials, one should question the rationale behind testing these interventions in early and prodromal subjects with low or zero amyloid load.

KEYWORDS:

Amyloid load; Amyloid secretase inhibition; Biologics; Modeling; Patient selection; Prevention trials; Trial failure

PMID:
29394903
PMCID:
PMC5797372
DOI:
10.1186/s13195-018-0343-5
[Indexed for MEDLINE]
Free PMC Article

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