Learning from amyloid trials in Alzheimer's disease. A virtual patient analysis using a quantitative systems pharmacology approach

Abstract

Background: Many trials of amyloid-modulating agents fail to improve cognitive outcome in Alzheimer's disease despite substantial reduction of amyloid β levels.

Methods: We applied a mechanism-based Quantitative Systems Pharmacology model exploring the pharmacodynamic interactions of apolipoprotein E (APOE), Catechol -O -methyl Transferase (COMTVal158Met), and 5-HT transporter (5-HTTLPR) rs25531 genotypes and aducanumab.

Results: The model predicts large clinical variability. Anticipated placebo differences on Alzheimer's Disease Assessment Scale (ADAS)-COG in the aducanumab ENGAGE and EMERGE ranged from 0.77 worsening to 1.56 points improvement, depending on the genotype-comedication combination. 5-HTTLPR L/L subjects are found to be the most resilient. Virtual patient simulations suggest improvements over placebo between 4% and 20% at the 10 mg/kg dose, depending on the imbalance of the 5-HTTLPR genotype and exposure. In the Phase II PRIME trial, maximal anticipated placebo difference at 10 mg/kg ranges from 0.3 worsening to 5.3 points improvement.

Discussion: These virtual patient simulations, once validated against clinical data, could lead to better informed future clinical trial designs.

Keywords: aducanumab; genotype; medication; pharmacodynamic effect; responder profile.

FIGURE 1

Schematic representation of the virtual patient platform. The core computer model consists of a QSP model that simulates the effect of actual Aβ loads on glutamatergic and nicotinic neurotransmission in an ADAS‐Cog calibrated neuronal cortical network. The input is defined as the number of patients, average baseline amyloid level and variance, their average rate of amyloid accumulation and variance, pharmacodynamic effect of amyloid agents on levels of both oligomeric Aβ40 and Aβ42, specific clinical trial design, fraction of patients on AChE‐I, and genotype distribution (in this example only APOE, COMTVal158Met and 5‐HTTLPR as an example).Changes in Aβ oligomeric load can be calculated from natural history in the placebo arm and pharmacodynamic effects of therapeutic interventions. The output is an individualized cognitive ADAS‐Cog trajectory for that specific patient

FIGURE 2

Effect of baseline amyloid and rate of amyloid accumulation on changes in ADAS‐Cog for aducanumab and placebo in ADAS‐Cog averaged over the 54 genotypes and comedications. Shown is the difference in changes versus baseline between aducanumab and placebo (positive outcome favors aducanumab). Note that the average changes in ADAS‐Cog from baseline are around 7 and 10.5 points for placebo at 52 and 104 weeks, respectively. The titration schedule of the Phase III ENGAGE and EMERGE trial at 10 mg/kg at 52 weeks (left) and 104 weeks (right) is used for different reductions of oligomeric amyloid accumulation rate by aducanumab (40%‐80%). Higher reductions increase the cognitive improvement over placebo to a maximum of 1.6 points for fast progressors and relatively low baseline (3‐4 units, around the threshold for amyloid PET positivity). Note that the clinical trial included patients with Aβ positivity (baseline >3)

FIGURE 3

Responder analysis for aducanumab. Frequency of alleles in top 30% of responder combinations. When rank ordering the different genotype‐medication combinations for their greatest improvement over placebo for both 52 and 104 weeks trial duration, subjects with the 5‐HTTLPR LL genotype are highly represented in the top 30%. Red lines correspond to the expected random distribution

FIGURE 4

Best, worse, and average scenario for the improvement over placebo after 52 weeks in the PRIME Phase II trial for four different doses for fast progressors with a baseline amyloid level of 3 units (around the threshold for amyloid positivity as explained in the text). Shown is the difference in changes versus baseline between aducanumab and placebo (positive outcome favors aducanumab). Because of the low numbers in the trial, differences between the active arm and placebo can vary greatly depending on the genotype and medication distribution. A maximal improvement of 5 points on the ADAS‐Cog can be achieved with the 10 mg/kg dose, whereas the worst‐case scenario will result in an over 3 point worsening for the 1 mg/kg dose

FIGURE 5

Virtual patient trial outcome after 52 weeks for changes in ADAS‐Cog as a function of baseline ADAS‐Cog. The genotypes/medications are distributed according to their incidence in the population, and both baseline and amyloid accumulation are sampled from a Gaussian distribution around the average value (here a baseline level of 6 units) and an amyloid accumulation of 1 unit/12 weeks. (Left) Subjects in brown are carriers of the 5‐HTTTLPR LL genotype. As expected, the worsening in ADAS‐Cog with amyloid accumulation decreases as the baseline ADAS‐Cog gets worse. However, 5‐HTTLPR LL carriers are more resilient to functional cognitive worsening for a similar amount of amyloid accumulation. (Right) There is little difference in the distribution of responders with (brown) or without (blue) the APOE44 homozygote genotype

FIGURE 6

Anticipated improvement over placebo at 52 weeks for a virtual patient trial (n = 1500) with an average baseline of 3 units and an amyloid accumulation of 10 weeks/unit, both sampled from a distribution with 50% variance. Shown is the difference in changes versus baseline between aducanumab and placebo (positive outcome favors aducanumab).The figure suggests that the highest exposure (80% inhibition of oligomer formation) in combination with an imbalance of only 40 responder subjects (of 400 possible subjects) for the 5‐HTTLPR L/L genotype can lead to an improvement over placebo of close to 20% at 52 weeks. Conversely, low exposure with an unfavorable distribution only leads to a 2%‐3% improvement over placebo