1. Safety assessment

2. Safety and efficacy What is the most important? You can’t have one without the other! Equally important but with very different characteristics

3. Safety and efficacy Hypotheses for efficacy are (should be) well-defined Clinical trials are most often dimensioned for the primary efficacy variable Hypotheses concerning safety assessments are less well-defined Dimensioning for safety would require very large studies, since also very rare events could be of outmost importance

5. Safety assessments We don’t know what we are looking for, just that we will not like it when we find it! Adverse events Laboratory data (bilirubin, creatinine, calcium, haemoglobin,…) Vital signs (pulse, blood pressure) ECG Physical examination (skin, head and neck, lungs,…)

6. Extent of exposure How many? –Number of patients exposed to drug How long? –Duration of exposure How much? –Dose Toxicity =f (exposure)

7. Extent of exposure: Examples Number of subjectsNumber of dosesDoses received 8260 mg + 480 mg 82120 mg + 600 mg 82240 mg Treatment A (n = 1698) Treatment B (n = 1699) Duration (patient years)Total2255.42336.1 Duration (days)Total823222852670 Mean485502 SD170145 Range2-7431-730

8. Terminology for adverse events Adverse event Adverse experience Serious adverse event Severe adverse event Risk Significant adverse event Toxicities Side effect Adverse drug reaction

9. Adverse event Definition (ICH): An adverse event is any untoward medical occurrence in a patient or clinical investigational subject administered a pharmaceutical product and that does not necessarily have a causal relationship with this treatment. An AE can therefore be any unfavorable and unintended sign (including an abnormal laboratory finding), symptom, or disease temporally associated with the use of a medicinal (investigational) product, whether or not it is related to the medicinal (investigational) product.

10. Serious adverse event Definition (ICH): A serious adverse event or reaction is any untoward medical occurrence that any dose: –Results in death –Is life-threatening –Requires inpatient hospitalization or prolongation of existing hospitalisation –Results in persistent of significant disability/incapacity –Is a congenital anomaly/birth defect

11. Coding of adverse events Dictionaries needed in order to group similar events Many different dictionaries available: Coding Symbols for a Theaurus of a Adverse Reaction Term (COSTART) World Health Orginasation Adverse Reaction Terminology (WHOART) Medical Dictionary for Regulatory Activities (MedDRA)

12. MedDRA coding MedDRA Structure Hierarchy System Organ Class (SOC) High Level Group Term (HLGT) High Level Term (HLT) Preferred Term (PT) Lowest Level Term (LLT) Example Infections and infestations Infections – pathogen unspecified Upper respiratory tract infections Nasopharyngitis Common cold

13. Types of adverse events Different types of events: Absorbing events (e.g. death) –Probability of occurrence Recurrent events with negligible duration (e.g. ) –Number of events Recurrent events with nonnegligible duration (e.g. headache) –Proportion of time affected Different measures and analysis are relevant!

14. Analysis of adverse events Descriptive statistics often the primary approach –Number and proportion of AEs –Number and proportion of patients with AEs Rates of occurrence of adverse events –Number of events per patient years Relative risks used to compare treatments –Hazard rate –Stratified Mantel-Haenszel estimate of relative risk

15. Treatment A N = 87 Treatment B N = 83 Number (%) of patients with AE Any AE57 (65.5%)64 (77.1%) SAE with outcome death0 (0%)1 (1.2%) SAE5 (5.7%)4 (4.8%) AE leading to discontinuation of study drug4 (4.6%)11 (13.3%) AE leading to discontinuation of study0 (0%) Total number of AEs Any AE133184 SAE65 Number (%) of patients with at least one AE and total number of AEs

16. Analysis of adverse events Treatment A N = 87 Treatment B N = 83 Patients with any SAE 5 (5.7%)4 (4.8%) Preferred term Back pain 1 (1.1%)1 (1.2%) Cerebrovascular accident 1 (1.1%) Hypotension 2 (2.3%)1 (1.2%) Lower limb fracture 1 (1.2%) Major depression 1 (1.1%) Pneumonia 1 (1.1%)2 (2.4%) Number (%) of patients with SAEs by preferred term

17. Estimating relative risk Assume k trials, each with two treatments (A and B). The relative risk of a certain (absorbing!) event is assumed to be the same in each trial and the number of events are assumed to follow a Poisson distribution. –i = 1,…,k (trials); j = A,B (treatments) –E ij = number of events in trial i on treatment j –e ij = observed number of events in trial i on treatment j –t ij = total exposure time in trial i on treatment j –E ij Po(λ ij t ij ) –Var[E ij ] = λ ij t ij –λ ij = e ij / t ij

18. The Mantel-Haenszel estimate Mantel-Haenszel estimate (stratified by trial) of the common relative risk, RR MH : λ j = Σ i w i · λ ij / Σ i w i w i = 2 / (1/t iA + 1/t iB ) RR MH = λ A / λ B log RR MH = log λ A – log λ B

19. Variance Variance of the Mantel-Haenszel estimate : Var [λ j ] = (Σ i w i 2 · e ij / t ij 2 ) / (Σ i w i ) 2 Var [log λ j ] = 1/λ j 2 · Var [λ j ] = (Σ i w i 2 · e ij / t ij 2 ) / (Σ i w i · e ij / t ij ) 2

20. Confidence intervals Confidence intervals: CI λ j = λ j ± 1.96 · (Var [λ j ]) 1/2 CI λ A - λ B = λ A - λ B ± 1.96 · (Var [λ A ] + Var [λ B ]) 1/2 CI logRR MH = log λ A - log λ B ± 1.96 · (Var [log λ A ] + Var [log λ B ]) 1/2 CI RR MH = λ A / λ B · exp { ±1.96 · (Var [log λ A ] + Var [log λ B ]) 1/2 }

21. Example Treatment ATreatment B NEventsExposure (pt yrs) Event rate (per pt yr) NEventsExposure (pt yrs) Event rate (per pt yr) Trial 132511101.70.10885135.70.028 Trial 286012.20.0004916.90.145 Trial 330710411.50.0242527340.10.021 Trial 4153332.30.09374117.90.056 Summary of exposure and number of patients with renal impairment

22. Example Estimate95% confidence interval Treatment A0.037[0.025, 0.056] Treatment B0.026[0.014, 0.048] Estimate95% confidence interval Risk difference (A-B)0.011[-0.0107, 0.0335] Relative risk (A versus B)1.44[0.68, 3.05] Mantel-Haenszel estimates of the event rate (per pt yr) for renal impairment Mantel-Haenszel estimates of the risk for renal impairment for treatment A compared to treatment B

23. Lab Variable = Variable indicating biological function observed from biological sample observed from biological sample (analysed in a lab) (analysed in a lab) Safety variables Adverse Events Adverse Events Vital signs (pulse, weight, blood pressure) Vital signs (pulse, weight, blood pressure) Labvariables by organ class Labvariables by organ class

24. Clinical chemistryHaematology aspartate aminotransferase, alanine haemoglobin, aminotransferase, alkaline haptoglobin, leukocyte count, thrombocyte phosphatase, bilirubin (total), count, reticulocyte count, leukocyte creatinine, thyroid stimulating differential count, mean corpuscular hormone, thyroxin (free), urate, albumin, C-reactive protein volume, mean corpuscular haemoglobin concentration glucose, sodium, potassium, calcium (albumin corrected), creatine kinaseUrinalysis protein, glucose, haemoglobin

25. Reference limits Lower limit of normal Upper limit of normal May differ between males, females, agegroups Created from a population data set, often specific to each lab leading to different labs having different reference limits Used to indicate abnormal values.

26. What are we looking for? Shift in population agerage Shift in population agerage Reactions in few sensitive patients Reactions in few sensitive patients Long term gradual effects Long term gradual effects Actue toxic reactios Actue toxic reactios

27. Examples An increase in the percentage of patients with ALT>3*ULN indicates potential liver issues developing over time. No indication of acute effect of a single dose on the liver.

28. Examples Liver values for a single patient

29. Pre-doseLast visit a Change b Lab variable (unit)TreatmentMean (SD) ALT (µkat/L )AZDXXXX 25 mg0.51 (0.30)0.52 (0.30)0.01 (0.22) AZDXXXX50 mg0.50 (0.27)0.54 (0.30)0.05 (0.21) AZDXXXX5 75 mg0.51 (0.29)0.53 (0.34)0.02 (0.22) AST (µkat/L )AZDXXXX5 25 mg0.41 (0.20)0.41 (0.15)0.00 (0.17) AZDXXXX5 50 mg0.39 (0.14)0.42 (0.18)0.03 (0.13) AZDXXXX5 75 mg0.40 (0.15)0.41 (0.16)0.01 (0.14) ALP (µkat/L )AZDXXXX5 25 mg1.37 (0.40)1.34 (0.39)-0.03 (0.18) AZDXXXX5 50 mg1.35 (0.40)1.34 (0.39)-0.01 (0.17) AZDXXXX5 75 mg1.36 (0.39)1.33 (0.37)-0.02 (0.19) CK (µkat/L )AZDXXXX5 25 mg2.84 (14.54)2.18 (1.57)-0.66 (14.36) AZDXXXX5 50 mg2.20 (1.55)2.17 (1.77)-0.03 (1.81) AZDXXXX5 75 mg2.13 (1.44)2.14 (1.37)0.01 (1.37) Creatinine (µmol/L )AZDXXXX5 25 mg78.80 (17.45)77.42 (16.71)-1.38 (11.00) AZDXXXX5 50 mg80.52 (18.03)78.12 (16.06)-2.40 (13.78) AZDXXXX5 75 mg80.31 (17.59)78.38 (16.48)-1.92 (10.54) Bilirubin, tot (µmol/L )AZDXXXX5 25 mg9.66 (4.53)9.56 (4.81)-0.10 (3.57) AZDXXXX5 50 mg10.29 (5.36)9.33 (4.45)-0.97 (3.82) AZDXXXX5 75 mg9.69 (4.80)8.90 (4.00)-0.79 (3.55) Sodium (mmol/L )AZDXXXX5 25 mg142.02 (2.51)142.42 (2.75)0.41 (2.90) AZDXXXX5 50 mg142.11 (2.49)142.25 (2.71)0.14 (2.70) AZDXXXX5 75 mg142.10 (2.38)142.52 (2.57)0.42 (2.75) Table 1Pre-dose and last visit observations of clinical chemistry variables, Safety Population

30. Issues Multiplicity – to test or not to test Multiplicity – to test or not to test Population vs individual – one patient can kill the drug Population vs individual – one patient can kill the drug Data cleaning Data cleaning What is extreme – multiplicity again What is extreme – multiplicity again