Design and Analysis of Crossover Study Designs Bhargava Kandala Department of Pharmaceutics College of Pharmacy , UF Bioequivalence (BE) refers to the absence of a significant difference in the rate and extent at which the drug becomes available at the site of action i.e. how much drug reaches the site of action and at what rate. Traditionally, pharmacokinetic studies have been the method of choice to carry out bioequivalence testing of a test and a reference product for systemically acting drugs. Where in the test and the reference are administered to the subjects and the amount of drug in their blood samples collected at specific time points are calculated. Metrics such as area under the curve and the maximum concentration achieved are used to test bioequivalence. The same approach is not used to establish bioequivalence of locally (lung) acting drugs such as Inhaled Corticosteroids (ICS). The reason behind it becomes clear when we look at the fate of an inhaled drug.

Crossover Study Treatments administered in a sequence to each experimental unit over a set of time periods. Comparison of treatments on a within-subject level. Increased precision of treatment comparisons. A treatment given in one period might influence the response in the following treatment period – residual/carryover effect Baseline values – Can be included as covariates to increase the precision

Study Design Single center, double blind, randomized, 3 period, 3 treatment, 3 sequence crossover study Randomization Low Medium High Washout Subjects = 10 Baseline 1 Period 1 (q.d.)   Period 2 (q.d.) Period 3 (q.d.) Baseline 2 Baseline 3 PD Measurements 5 days 1 Week 5 days 1 Week 5 days

Model for Crossover Design   Period 1 2 3 4 5 6 I A B C II III  

proc glm data = allperiodanaly; class sequence subject period trt; model fenoav = sequence subject(sequence) period trt/solution; random subject(sequence); run;

proc mixed data = allperiodanaly; class sequence subject period trt; model fenoav = sequence period trt; random subject(sequence); lsmeans trt/ pdiff cl; run;

Baseline  

Analysis of Covariance (ANCOVA) Baseline - Covariate Average baseline values not significantly different Presence of significant carryover effects (p-value < 0.05) No Covariate Analysis of Covariance (ANCOVA) Baseline – Treatment β = 0 β = Model Estimate β =1 Baseline is not used as a covariate Baseline values are treated as a quantitative variable By taking the simple difference the value of β is forced to be 1

Carryover Effect  

* Covariates tested for carryover; proc mixed data = allperiodanaly; class sequence subject period trt; model fenoav = sequence period fenob trt carry1 carry2; random subject(sequence); lsmeans trt/ pdiff cl e; run;

Analysis of Covariance (ANCOVA) Results β cannot be forced to be 1 Parameter No Covariate Analysis of Covariance (ANCOVA) Baseline – Treatment β 0.38 1 Residual Variability 85.39 67.02 180.02 Carryover Effect Not significant (p-value >0.05) Not Significant (p-value>0.05) Significant

Analysis of Covariance (ANCOVA) Results   Parameter No Covariate Analysis of Covariance (ANCOVA) Baseline – Treatment β 0.38 1 Residual Variability 85.39 67.02 180.02 Carryover Effect Not significant (p-value >0.05) Not Significant (p-value>0.05) Significant

Analysis of Covariance (ANCOVA) Results Reduced impact of the baseline values while using ANCOVA can explain the absence of carryover effects Parameter No Covariate Analysis of Covariance (ANCOVA) Baseline – Treatment β 0.38 1 Residual Variability 85.39 67.02 180.02 Carryover Effect Not significant (p-value >0.05) Not Significant (p-value>0.05) Significant

Conclusions Day 5 data suitable for analysis Baseline adjustment Maximum dose resolution No carryover effect Baseline adjustment Simple difference increases the variability and introduces carryover effects ANCOVA is the preferred method Crossover design model with baseline values as covariates will be used for future simulations