CENTER FOR DRUG EVALUATION AND RESEARCH APPLICATION NUMBER: 211230Orig1s000 211230Orig2s000 CLINICAL REVIEW(S) REVIEW OF CLINICAL DATA NDA #211230 Brand name (generic name) Sunosi (solriamfetol) Sponsor Jazz Pharmaceuticals Materials reviewed DCRP Consult review, NDA submission Reviewer Marc Stone, MD Deputy Director for Safety Date Completed 19 March 2019 Background Solriamfetol is a drug with stimulant-like effects being considered for the indication of excessive daytime sleepiness in patients with 1) narcolepsy or 2) obstructive sleep apnea. The product appears to be efficacious for these indications. A major review concern has been the effects of solriamfetol on heart rate and blood pressure. The drug is intended for long-term use and the effects of heart rate and blood pressure may increase cardiovascular risk, particularly for patients with other risk factors. The two principal studies for the respective indications, 14-002 and 14-003, incorporated ambulatory blood pressure monitoring (ABPM). The Applicant conducted analyses of standard vital sign measurements as well as ABPM in its Integrated Summary of Safety (ISS). The Division of Cardiorenal Products (DCRP) was consulted to describe and interpret the Sponsor’s analyses and provide recommendations about risk implications for labeling. The thorough QT study for solriamfetol (Study 15-002) was reviewed by the Interdisciplinary Review Team for QT Studies (IRT/QT). The review noted that the solriamfetol dosages used in the study (300 mg and 900 mg) were associated with large increases in heart rate (estimated by them as a mean increase relative to baseline and placebo as 12.8 bpm for 300 mg and 19.5 bpm for 900 mg). With such large changes in heart rate, the Fridericia correction used in the study has questionable accuracy, resulting in considerable uncertainty as to the magnitude of the effect, especially at larger concentrations. Analytical challenges with the submission Because of the variability in who, when, where and how measurements of blood pressure are made, it is difficult to characterize drug effects on blood pressure with reliability and precision. The clinic-based data, as presented by the Sponsor, do not take these complexities into account. In the Sponsor’s Table 181 from the ISS, the simple means of all measurements obtained in all subjects at a given visit are averaged. Two narcolepsy studies are combined even though only one included a 75 mg dosage and different subjects are averaged at different time points – note the decrease in the number of narcolepsy subjects between 4 and 8 weeks for the 150 mg dose and the increase for 300mg. No attempt is made to account for within-individual variability. A similar approach was taken on days when multiple measurements (pre-dose and 1, 2, 4, 6, 8, and 10 hours post- dose) were made (Sponsor’s Table 183). There is little reason to assess blood pressure effects separately at different weeks; the effects are not thought to be cumulative. Solriamfetol is intended to be relatively short acting: with once daily dosing and a half-life of about seven hours, its effect is intended to wear off by bedtime to avoid interference with intended sleep. Reference ID: 4406170 APPEARS THIS WAY ON ORIGINAL Reference ID: 4406170 Reference ID: 4406170 A larger number of observations were obtained using 24-hour ABPM on two occasions in Studies 14-002 and 14-003, at baseline and after eight weeks of treatment but the analyses once again relied upon overall means by dosage and did not look at changes within individual subjects. From the Statistical Analysis Plan: For the ABPM BP and HR data obtained during Screening and Week 8 at 30 minute interval during a 24 hour period, the overall mean, mean during the daytime period from 7:00 to 22:00 and mean during the nighttime period from 22:00 to 7:00 will be summarized by treatment group. ABPM provides many readings in a short period of time but cannot control the conditions under which the measurements are obtained such as with position, exercise or emotion. In this case, data assessed in this manner can possibly give a general impression of impact on vital signs but provide no insight into either statistical uncertainty or comparability (because there is no attempt to account for variability due to measurement variability among studies and time points and variability within and between subjects). The analysis of drug effects on QT is complicated by the relationship between the length of the QT interval and heart rate (RR interval). Hence the measured length of the QT interval must be adjusted for heart rate. The Fridericia correction is based on the formula: QTc=QT/RR1/3 which means QT=QTc*RR1/3, ln(QT)= ln(QTc) + ln(RR)/3 and ln(QTc)=ln(QT)-ln(RR)/3. However, the relationship between QT and RR is frequently different than what is given by the Fridericia correction. The Bazett correction uses an exponent of 0.5. These analyses also assume that there is no variation among subjects in the relationship between QT and RR. Methods used in this review Clinic Vital Signs I assembled all data on heart rate and blood pressure collected in the Advs files for 15 studies conducted in human subjects in this NDA submission. Studies were not included if they only obtained baseline or pre-dose measurements. Study Subjects BP Measurements Pulse Measurements 14-001 107 4,219 4.219 14-002 239 19,763 13,196 14-003 476 38,866 25,972 14-004 174 12,170 8,178 14-005 645 10,541 8,969 15-001 31 348 348 15-009 32 365 365 ADX-N05-201 36 463 465 ADX-N05-202 93 3,148 3,146 (b) (4) NED-1 123 4,407 4,407 (b) (4) P01-101 4 32 32 (b) (4) SAB-101 26 2,654 2,654 (b) (4) USA-10 110 1,249 1,249 Reference ID: 4406170 (b) (4) -9603-01 24 1,230 1,230 (b) (4) -9702-01 50 347 347 Total 1,531* 99,802 74,777 *639 subjects in 14-005 were recruited from other studies Statistical analyses were performed using hierarchical mixed models for repeated measurements (MMRM). This method allows for the difference in observed values when exposed to different drug dosages compared to no drug exposure (the fixed effect) to be estimated under a variety of different circumstances (random effects), estimating the variance due to the variety of circumstances and separating that variance from that seen in the fixed effect. The random effects used in the models include: Study: Studies are assumed to involve different populations and each population has a different mean value for each vital sign exclusive of drug effect. The variability among studies is assumed to follow a random normal distribution. Subject: Within study populations, each individual subject also has a different mean value exclusive of drug effect. It is also assumed to follow a random normal distribution. Inter-subject variability is nested within study. Position: For studies that made observations at different positions (i.e., standing or supine), individual subjects maintain the same orthostatic differences over time, but this difference can vary among subjects. Study day: Mean values for vital signs for subjects exclusive of drug effect vary from day to day and are assumed to follow a random normal distribution. Day to day variability is nested within position, subject and study. Time: Vital sign values exclusive of drug effects vary in general over the course of the day and similarly for all subjects. This also stratifies calculations so that the observed drug effect at each time point is given similar weight in the overall mean calculation. For assessment of general trend in dose-response, dosage was treated as a continuous variable. To look for non-linearity, a model was fit using multivariate adaptive regression splines, but little difference was found between linear models and spline-based models. For threshold effects the mixed model calculated odds ratios but because of computational difficulties, the model was simplified to use just subjects nested in time as random effects. Ambulatory Blood Pressure Monitoring The data provided by the Sponsor consisted of an average of up to four measurements obtained over 2-hour intervals. The combined dataset for the two ABPM studies consisted of 87,650 observations for each parameter in 705 subjects. I analyzed this dataset with a similar hierarchical MMRM but with only, study, subject and time as random effects. To compare ABPM results to results from clinic readings I considered the 8 am to 8 pm period for ABPM to correspond to the maximum twelve-hour observation period used with clinic measurements. Thorough QT Study Because there are multiple baseline ECGs for each subject and additional ECGs with negligible or no drug exposure (when the measured plasma solriamfetol concentration is zero (and the subject has not received moxifloxacin)), the individual relationship between QT and RR when patients are untreated can be estimated by linear regression Reference ID: 4406170 of ln(RR) on ln(QT) where the slope is the estimate of the power to which RR is raised (fixed at 1/3 by the Fridericia equation) and the intercept is the estimate of the logarithm of QTc (QT when RR is one second). A simple linear regression for each subject would be imprecise as it would be based on very limited data. This noise can be reduced by using MMRM with ln(RR) entered into the model as a random effect. Any drug effect is estimated by adding a dummy variable for drug exposure which would estimate a percentage change in QTc due to drug effect or a continuous variable for plasma concentration which would estimate a percentage change in QTc due to a given plasma concentration.