Leveraging External Data in Drug Development for Rare Diseases
Meenakshi Srinivasan1, Navin Goyal2, Daren Austin2, Rashmi Mehta2, Rajendra Singh2
1University of Florida College of Pharmacy, Orlando, Florida, USA 2GlaxoSmithKline plc, Collegeville, PA, USA
Drug development in Rare Diseases is challenging given the prevalence,
limited data and understanding of relevant biomarkers, pharmacodynamics (PD)
and clinical endpoints.
Applying a Model Informed Drug Development (MIDD) framework by
leveraging published data from clinical studies can therefore provide critical
insights into efficient clinical study designs.
METHODS
Reduction in urinary tetranor-prostaglandin D metabolite (t-PGDM) from
baseline can establish desired proof of pharmacology for hPGDS inhibitors.
Data extraction: Mean and standard deviations of change in t-PGDM from
baseline across placebo and four dose levels of active drug were digitized from
the published Phase 1 and Phase 2 studies of TAS-205, a hPGDS inhibitor
under development (Takeshita et al., 2018; Komaki et al.,2020). Key study
features are provided in Table 1.
An exponential placebo model with Imax inhibitory drug effect was chosen with
adequate qualification. (Figure 1 and Table 2)
Placebo model: ๐ธ = ๐ธ๐ต๐ด๐๐ธ โ๐๐ธ โ (1 โ ๐โ๐พ๐โ 1โ๐ธ๐ท๐ ๐๐บ โ๐๐ผ๐๐ธ) , where
EBASE is the baseline value, which was fixed to 100, PE describes
magnitude of the placebo effect, KP is the rate constant characterizing the
rate of change in disease severity (placebo response) and TIME refers to
the time in hours.
Drug effect: ๐ธ๐ท๐ ๐๐บ =๐ผ๐๐ด๐+๐ท๐๐๐ธ
๐ผ๐ถ50+๐ท๐๐๐ธ, where IMAX is the maximum inhibitory
drug effect, DOSE is the absolute value of amount of drug normalized by
body weight and IC50 is the value of change in baseline tPGDM at 50% of
IMAX
Rate of change of biomarker decline differed across the two studies
OBJECTIVEThe current work describes considerations when leveraging literature data to
design clinical studies and estimate the probability of pharmacological success
(PoPS) for drugs with similar mechanisms of action.
An example of hematopoietic-prostaglandin D synthase (hPGDS) inhibitors in
Duchenne muscular dystrophy therapy is presented.
NONMEM dataset preparation: The change in the biomarker over time was
the dependent variable (DV) of interest. Within-patient time series are
correlated, hence a multivariate normal distribution was used to generate 100
random samples of the DV per treatment arm using the mvtnorm package in R.
For the DV, the covariance matrix with different correlation coefficients
were used (r = 0, 0.1, 0.5 and 0.9).
Covariates such as age and weight for each treatment group were used
to generate random samples using multivariate normal distribution (r =
0.8).
PD model development: Several PD models were evaluated for placebo
(exponential, power and Weibull) and drug effect (Imax and linear). Potential
difference in population between Phase 1 and Phase 2 studies was estimated
as a fixed effect.
Between subject variability was modeled as log normally distributed
Model assessment included goodness of fit plots, Akaike Information
Criteria (AIC) values, visual predictive checks, bootstrapping.
Phase 1 Phase 2
Sample size (n) 21 36
Age 5-15 years โฅ5 years
Treatment groups
(mg/kg/dose)
Step A (n=5): 1.67-3.33
Step B (n=5): 3.33-6.67
Step C (n=5): 6.67-13.33
Low dose (n=11): 6.67-13.33
High dose (n=12): 13.33-26.67
Sample size, placebo
group6 13
Dosing frequency,
periodBID, 7 days BID, 24 weeks
PD assessment time
pointsBaseline, day 1, 4 and 7
Baseline, day 4, week 12 and
week 24
Table 1. Features of clinical studies conducted for TAS-205
INTRODUCTION METHODS
Simulations: Clinical trial simulations for a hypothetical test drug were
conducted using the mrgsolve package in R
Patient enrollment challenges in in rare diseases were evaluated with different
sample sizes per trial were simulated: low (4-9 subjects) and high (10, 20 and 30
subjects) for an average 40 kg subject at two dose levels: low (6.67-13.33) and
high (13.33-26.67) mg/kg/dose.
PoPS was calculated as the proportion of trials with at least 50% (low threshold
criteria) and 90% (high threshold criteria) of participants in a trial achieving
greater than 30% decline in t-PGDM from baseline at 24 hours at various doses
of interest.
RESULTS
RESULTS RESULTS
Parameter Parameter definition Estimate
(%RSE)
Bootstrap median
(95%CI)
๐ฝ๐ฐ๐ด๐จ๐ฟMaximal inhibitory effect -19.9 (23.7) -20.1 (-34.8 - -13.2)
๐ฝ๐ฐ๐ช๐๐Half-maximal inhibitory
dose25.7 (30.9) 26.3 (15.4 โ 50.5)
๐ฝ๐ท๐ฌMagnitude of placebo effect 48.9 (1.1) 49 (47.8 โ 49.9)
๐ฝ๐ฒ๐ทRate constant of rate of
change in disease severity0.00645 (4.2) 0.0064 (0.006-0.007)
๐ฝ๐บ๐ป๐ผ๐ซ๐Fixed effect of study on KP 0.00776 (7.4) 0.0078 (0.0067-0.009)
๐ฌ๐ฉ๐จ๐บ๐ฌ Baseline effect 100 100
๐๐ฐ๐ด๐จ๐ฟ๐ Interindividual variability on
IMAX (%CV)93.4 (12.8) 93.1 (80.4-106.3)
๐๐ฒ๐ท๐ Interindividual variability on
KP (%CV)33.6 (30.1) 33.7 (22.6-43.5)
๐๐ท๐น๐ถ๐ท๐ Proportional error (%CV) 20.5 (4.6) 20.5 (19.5 โ 21.4)
๐๐จ๐ซ๐ซ๐
Additive error 10.3 (75.8) 10.3 (0.001-30.8)
CONCLUSIONS
REFERENCES
1. Takeshita, E., Komaki, H., Shimizu-Motohashi, Y., Ishiyama, A., Sasaki, M., & Takeda, S. (2018). A phase I
study of TAS-205 in patients with Duchenne muscular dystrophy. Annals of Clinical and Translational Neurology,
5(11), 1338โ1349. https://doi.org/10.1002/acn3.651
2. Komaki, H., Maegaki, Y., Matsumura, T., Shiraishi, K., Awano, H., Nakamura, A., Kinoshita, S., Ogata, K.,
Ishigaki, K., Saitoh, S., Funato, M., Kuru, S., Nakayama, T., Iwata, Y., Yajima, H., & Takeda, S. (2020). Early
phase 2 trial of TAS-205 in patients with Duchenne muscular dystrophy. Annals of Clinical and Translational
Neurology, 7(2), 181โ190. https://doi.org/10.1002/acn3.50978
Biomarker inhibition increases with dose. Due to high variability in the data, the
two dose levels lack clear separation in their PD effects.
Higher the sample size, higher is the precision in estimation of the effect.
(Figure 2)
Figure 2. Effect versus time profile for simulations. Solid lines are the lower 2.5th
percentile, median and upper 97.5th percentile at each time point. Shaded bands are the 95%
CI around these percentiles are also plotted. Plots faceted by sample size.
Around 55-60% of subjects from low dose and 66-70% from high dose reach
the response criteria at 24 hours.
At lower sample size, it is not possible to distinguish between the low and high
dose. However, as the sample size increases, the distinction between the dose
levels can be observed, though not clearly separated due to the high variability.
(Figure 3)
Trial simulations conducted with smaller sample sizes may result in potentially
erroneous PoPS conclusions.
Although increasing trial sample size naturally improves power to estimate the
true PoPS, such trials may be impractical in rare diseases.
Trial to trial and population differences between studies, variability in PD
response and sample size have significant impact on PoPS estimation.
Figure 3. Proportion of subjects achieving 10-50% decline of biomarker from baseline at
24 hours. Plots faceted by sample size. Solid line represents the median proportion and the
bands represent the 95% CI around the proportion.
Counterintuitively, using the 90% success criteria, PoPS decreased with
increasing sample size and was <15% at 24 h for both high- and low-dose
groups. This could potentially be a trial design artifact, with a greater number of
possible combinations resulting in trial failures as sample size increases (Figure
5). No orderly trends with 4-9 subjects were observed in PoPS (<20% at 24 h)
(Figure 6).
Larger study sample size with the 50% change from baseline criteria led to a
considerably higher PoPS for the high-dose group, compared to low-dose
(Figure 1). Within each dose group, PoPS increased with higher sample size.
Figure 4. Probability of success: Proportion of simulations with at least 50% of subjects
reaching responder criteria, considering sample sizes of 10, 20 and 30 per trial. Note: In
this case success is defined as probability that 50% subjects achieve greater than or equal to
30% reduction in tPGDM/Cre from baseline. Plots faceted by dose level
Figure 5. Probability of success: Proportion of simulations with at least 90% of subjects
reaching responder criteria, considering sample sizes of 10, 20 and 30 per trial. Note: In
this case success is defined as probability that 90% subjects achieve greater than or equal to
30% reduction in tPGDM/Cre from baseline. Plots faceted by dose level
Figure 6. Probability of success: Proportion of simulations with at least 90% of subjects
reaching responder criteria, considering sample sizes of 4-10 per trial. Note: In this case
success is defined as probability that 90% subjects achieve greater than or equal to 30%
reduction in tPGDM/Cre from baseline. Plots faceted by dose level
Figure 1. VPC plot for
Phase 1 and Phase 2
data by treatment arm.
Step A, B and C, Low
and High dose are the
treatment arm for Phase
1 and 2 data (Table 1).
The blue dots represent
the observations, lines
represent the 90%
percentiles of the
simulated data and the
bands around the lines
represent the 90%
confidence intervals
around these quantiles.
Table 2. Final model parameter estimates
DISCLOSURESM.S is funded through a University of Florida/GlaxoSmithKline Pharmacokinetics/Pharmacodynamics Post-Doctoral
Fellowship. N.G and R.S are former employees of and past/current shareholder in GlaxoSmithKline plc. D.A and R.M
are employees of and shareholders in GlaxoSmithKline plc.