G.L. Drusano, M.D. Co-Director Ordway Research Institute Professor and Director

1. Convincing the Pharmaceutical Industry to Use Surrogates for Antibiotic Development: What is Gained and What is Lost G.L. Drusano, M.D. Co-Director Ordway Research Institute Professor and Director Division of Clinical Pharmacology Albany Medical College Research Physician Wadsworth Center, David Axelrod Institute New York State Department of Health

2. Surrogates & Antibiotic Development • First, let me state my understanding of “surrogates” • It is important to acknowledge the very nice slide deck of Dr. Arthur Atkinson posted on the WEB • For antibiotic trials, we can have a surrogate endpoint as well as a biomarker (or surrogate marker, if you do not mind bucking the following guys, who will be happy to meet you out back) BIOMARKERS DEFINITIONS WORKING GROUP: BIOMARKERS AND SURROGATE ENDPOINTS: PREFERRED DEFINITIONS AND CONCEPTUAL FRAMEWORK. CLIN PHARMACOL THER 2001;69:89-95. The surrogate endpoint can be microbiological outcome instead of clinical outcome. The biomarker can be a transformation of the measured drug concentration (e.g. free drug AUC/MIC ratio or free drug Time > MIC) All of the above is definition driven by statisticians

3. Surrogates & Antibiotic Development • So, for antibiotic studies, we can measure a biomarker and we can use a surrogate endpoint • While measuring the biomarker is straightforward, there are issues about the surrogate endpoint (discussed shortly) • Why do we do reasonably well with identifying exposure-response relationships in anti-infective trials? • The answer is that the MIC gives us a normalizing measure for docking the same drug into different receptors (different bacteria)

4. Surrogates & Antibiotic Development • For antibiotics, we try to develop relationships between some measure of drug exposure and some measure of effect • Most often, these are clinical or microbiological outcome, both of which are dichotomous outcome variables

5. Surrogates & Antibiotic Development • We have seen Dr. Forrest present a large number of patient studies, BUT were one to perform a literature search, I would expect that the numbers of such studies would differ from the numbers of in vitro and animal studies by several orders of magnitude • Why the difference? • First, the former measure colony counts (a continuous variable) while the latter deal with dichotomous outcome variables

6. Surrogates & Antibiotic Development • Second, it is MUCH cheaper and faster to perform in vitro and animal studies • So, what can we gain from the former and what from the latter? • It is important to remember that the FDA, IDSA and ISAP recently held a workshop that addressed much of this • Most of the outcome can be addressed with one slide, after which I will guild the lilly

7. This is what the FDA expects from sponsors “Closing the Loop” In-Vitro/ Animal Models Phase 3 Studies Phase 2 Studies Phase 1 Studies Pre-Clinical Clinical Development Office of Clinical Pharmacology and Biopharmaceutics IDSA/ISAP/FDA Workshop 4/16/04 – Dr. Charles Bonapace

8. Surrogates & Antibiotic Development • Let us first examine preclinical studies • At the point of entry into Phase I trials, we are trying to choose a safe and effective dose for Phase II and Phase III trials • So, what do we need?♦ An exposure target (hence the preclinical studies ♦ Population pharmacokinetic information ♦ Protein binding data from animals & man ♦ Target organism MIC distribution

9. Drusano GL. Nat Rev Microbiol 2004;2:289-300 Journal of Clinical Investigation 2003;112:275-285 & Nature Reviews Microbiology 2004;2:289-300

10. 90 mg/kg 0 mg/kg 215 mg/kg 600 mg/kg Journal of Clinical Investigation 2003;112:275-285 & Nature Reviews Microbiology 2004;2:289-300 Jumbe et al J Clin Invest 2003;112:275-285

11. Peripheral (thigh) Compartment (Cp) Bacteria (XT/R) + kpc f(c) kcp Central Blood Compartment (Cc) dXS=KGS xXS x L - fKS(CcH) x XS dt [4] IP injection [5] dXR= KGR xXR x L- fKR(CcH) x XR dt [6] L = (1- (XR + XS)/POPMAX) ke KmaxCcH C H 50+CcH  dCa= -kaCa dt , =K and = S,R [1] [7] f(CcH)= dCc= kaCa+kpcCp-kcpCc-keCc dt [2] Y1=XT=XS+XR Y2=XR [8] dCp = kcpCc - kpc Cp dt [3] [9]

12. Mean Parameter Estimates of the Model. KmaxGS 0.117 KmaxGR 0.163 C50KS 123.5 C50KR 129.8 HKS 6.26 HKR 2.37 KmaxKS 94.01 KmaxKR 12.16 Popmax = 3.6 x 1010 KmaxG -maximum growth rate (hr-1) in the presence of drug KmaxK -maximum kill rate (hr-1) C50K -drug concentration (g/mL) to decrease kill rate by half HK -rate of concentration dependent kill Popmax -maximal population size

13. Jumbe et al J Clin Invest 2003;112:275-285 Drusano GL. Nat Rev Microbiol 2004;2:289-300

14. Surrogates & Antibiotic Development Jumbe et al J Clin Invest 2003;112:275-285 Drusano GL. Nat Rev Microbiol 2004;2:289-300

15. Jumbe et al J Clin Invest 2003;112:275-285 Drusano GL. Nat Rev Microbiol 2004;2:289-300 Journal of Clinical Investigation 2003;112:275-285 & Nature Reviews Microbiology 2004;2:289-300

16. Surrogates & Antibiotic Development Pre-Clinical to Phase I/II: What about Resistance? Can this be done in vitro?

17. Surrogates & Antibiotic Development The hollow fiber model was described by Blaser and Zinner and employed extensively by Dudley

18. Surrogates & Antibiotic Development Tam V et al. Bacterial-population responses to drug selective pressure: Examination of garenoxacin’s effect on Pseudomonas aeruginosa. J Infect Dis 2005;192:420-428

19. Surrogates & Antibiotic DevelopmentP. aeruginosa - Prevention of Amplification of Resistant Subpopulation • The amplification of the resistant sub-population is a function of the AUC/MIC ratio • The response curve is an inverted “U”. • The AUC/MIC ratio for resistant organism stasis is circa 185/1 Resistant organisms at baseline All other data points represent resistant organism counts at 48 hours of therapy

20. Surrogates & Antibiotic Development Propspective Validation Study Tam V et al. Bacterial-population responses to drug selective pressure: Examination of garenoxacin’s effect on Pseudomonas aeruginosa. J Infect Dis 2005;192:420-428

21. Surrogates & Antibiotic Development • We can bridge preclinically to Phase II/III through the use of Monte Carlo simulation • This is the plot of probability for target attainment (resistance-suppression exposure) for a FQ as derived from the preclinical model Jumbe et al J Clin Invest 2003;112:275-285

22. Surrogates & Antibiotic Development • The previous data was for resistance, but straightforward cell kill is also possible • Later we will see how much kill correlates with what happens in clinical trials • BUT, please remember that a total drug AUC/MIC = 88 gives a 2 log kill Jumbe et al J Clin Invest 2003;112:275-285

23. Surrogates & Antibiotic Development • We can also perform such studies in Phase II/III, as Dr. Forrest has already shown you • As an example, a trial of nosocomial pneumonia with the same FQ as in the mouse study will be presented

24. Population mean pharmacokinetic parameter values derived from 58 Patients with Nosocomial Pneumonia Receiving Levofloxacin as a 1.5 Hour Constant Rate, Intravenous Infusion Vol Kcp Kpc CL Units L hr-1 hr-1 L/hr Means 34.4 7.65 6.07 7.24 Medians 23.3 2.66 0.924 6.24 S.D. 33.5 9.59 12.0 4.36 Vol = Volume of the central compartment; Kcp and Kpc are first order ntercompartmental transfer rate constants connecting the central and peripheral compartments; CL = Total clearance of Levofloxacin Drusano GL, SL Preston, C Fowler, M Corrado, B Weisinger, J Kahn J Infect Dis. 2004;189:1590-1597.

25. Final model for microbiological outcome for patients with nosocomial pneumonia receiving levofloxacin daily Final Model for Microbiological Outcome Constant Parameter Odds Ratio 95% Confidence Interval for Odds Ratio (AUC/MIC > 87) -2.197 1.374 3.952 11.596 – 1.347 (Age) 0.067 1.069 1.138 - 1.004 p = 0.001; McFadden’s 2 = 0.31 Drusano GL, SL Preston, C Fowler, M Corrado, B Weisinger, J Kahn J Infect Dis. 2004;189:1590-1597.

26. Drusano GL, SL Preston, C Fowler, M Corrado, B Weisinger, J Kahn J Infect Dis. 2004;189:1590-1597.

27. Surrogates & Antibiotic Development Can we bridge from mouse to man? Again, the answer is YES, using Monte Carlo Simulation

28. The Reminder Slide Surrogates & Antibiotic Development • The previous data was for resistance, but straightforward cell kill is also possible • Later we will see how much kill correlates with what happens in clinical trials • BUT, please remember that a total drug AUC/MIC = 88 gives a 2 log kill Jumbe et al J Clin Invest 2003;112:275-285

29. Surrogates & Antibiotic Development • So, the exposure target (AUC/MIC) mediating a 2 log10 cfu/g drop in the mouse is identified as the exposure needed to drive a high probability of a good microbiological outcome in patients with nosocomial pneumonia • BUT the clinical study was a pneumonia study & the mouse study was mouse thigh infection • Andes and Craig showed for a FQ that Mouse thigh and mouse lung targets are virtually identical (AAC 2002;46:1665-1670) • How often does a fixed dose of drug achieve this target?

30. Drusano GL, SL Preston, C Fowler, M Corrado, B Weisinger, J Kahn J Infect Dis. 2004;189:1590-1597.

31. Drusano GL, SL Preston, C Fowler, M Corrado, B Weisinger, J Kahn J Infect Dis. 2004;189:1590-1597.

32. Surrogates & Antibiotic Development • So, what do we gain?♦ Information to choose the best dose ♦ A high likelihood that the Phase III trial will work (at the cost, that is worth something!) • What is there to lose? ♦ Nothing that I can see

33. Surrogates & Antibiotic Development • Let us examine the clinical trial result just presented and speculate about the future • We looked at a continuous microbiological variable in the preclinical study and a dichotomous microbiological variable in the clinical study (eradication vs. persistence) • Is the microbiological outcome from the clinical trial a surrogate outcome? • According to the working group the answer is yes

34. EXAMPLES OF LABORATORY ENDPOINTS THERAPEUTIC BIOMARKER/ CLINICAL CLASS SURROGATE_OUTCOME ANTIBIOTICS NEG. CULTURE CLINICAL CURE ANTI-DIABETIC  BLOOD GLUCOSE  MORBIDITY LIPID LOWERING DRUGS  CHOLESTEROL  CAD DRUGS FOR PROSTATE CA  PSA TUMOR RESPONSE ANTI-HIV DRUGS  CD4; VIRAL RNA DELAY AIDS From Arthur Atkinson, M.D.

35. Surrogates & Antibiotic Development • I know it is “the law” that approvals are only given on clinical outcomes, but is it reasonable? • Subpart H does existTITLE 21, PART 314, SUBPART HSEC. 314.510 APPROVAL BASED ON A SURROGATE ENDPOINT OR ON AN EFFECT ON A CLINICAL ENDPOINT OTHER THAN SURVIVAL OR IRREVERSIBLE MORBIDITY “FDA MAY GRANT MARKETING APPROVAL FOR A NEW DRUG PRODUCT ON THE BASIS OF ADEQUATE AND WELL-CONTROLLED CLINICAL TRIALS ESTABLISHING THAT THE DRUG PRODUCT HAS AN EFFECT ON A SURROGATE ENDPOINT THAT IS REASONABLY LIKELY … TO PREDICT CLINICAL BENEFIT…” • AND, the outcome for pharyngitis is COMPLETELY microbiological in nature • So, can trials for approval be smaller, faster, more informative because they are based on microbiological outcomes and still count for NDA submission/approval The answer may be addressed in the next talk • But, let us examine the issue

36. Surrogates & Antibiotic Development • Yes, I am a true statistical believer and following to a survivorship endpoint may have the most robustness for being sure that the intervention is doing somethingBUT, antibiotics (and antivirals) are a bit different from other drugs • We are NOT docking the drug with a human receptor, but rather the receptor in the pathogen

37. Surrogates & Antibiotic Development • BIOLOGICAL PLAUSIBILITY • EPIDMIOLOGIC EVIDENCE THAT MARKER IS A RISK FACTOR • MARKER MUST BE CONSISTENT WITH PATHOPHYSIOLOGY • MARKER MUST BE ON INTERVENTION PATHWAY • CHANGES IN MARKER REFLECT CHANGES IN PROGNOSIS • ADVERSE DRUG EFFECTS MUST NOT BE CONFOUNDING (again, thanks to Dr. Atkinson) • So, let us look at microbiological outcome as a surrogate endpointgrowing organisms in sterile areas is a risk the pathophysiology has the organism at the center of it the organism IS the target of the intervention making it go away improves prognosis adverse drug effects are confounding TO CLINICAL OUTCOME • Are there instances where the microbiological effects do not drive outcome clinically?

38. Surrogates & Antibiotic Development • Sure – nothing is perfect – macrolides and their effects on inflammation may make things better faster clinically in otitis media • BUT clinical outcome may also be skewed • In pneumonia, the impact of infection on gas exchange may be so far advanced that the drug does its job perfectly (eradicate organisms) but the patient dies of ventilatory failure • This is a physiological, NOT drug failure

39. Surrogates & Antibiotic Development • If the drug does what it is supposed to do can we scientifically impute failure? • Currently, this is not THAT big a deal, as clinical outcome and microbiological outcome are tightly intertwined because of the way they are measured • Eradication is frequently assessed as a good clinical outcome combined with no chance of primary infection site resampling • BUT, it may not always be so

40. Surrogates & Antibiotic Development • PCR tests, antigen tests, quantitative imaging testing or other methodologies have the possibility of having a quantitative resampling microbiological outcome • Dr. Forrest has published a study in which the lower respiratory tract was resampled sequentially • Dr. Paul Ambrose has published a quantitative resampling study of sinusitis • The future may be now

41. Surrogates & Antibiotic Development • Look at the results in HIV disease • MAJOR advances came about only AFTER quantitative RNA PCR was identified as a valid surrogate endpoint • The conversation needs to begin for antibacterials

42. Surrogates & Antibiotic Development • The insistence on clinical endpoints instead of microbiological endpoints is driven by two things:1) Many statisticians are nihilists – see the silliness of intent-to-treat2) There is a confounding of safety with effectiveness

43. Surrogates & Antibiotic Development • I believe that we CAN safely use prior information to conduct our clinical trials (In this I am a Bayesian) • Nihilistic ITT statisticians say that we need to be ULTRA sure of the validity of the results and, so biological plausibility and prior knowledge need to go out the window • The question is: How many times do we reject the true outcome because of statistical nihilism?

44. Surrogates & Antibiotic Development • What causes more harm? • The other issue is confounding endpoints of safety and effect • It is true that in HIV disease, patients with MAC had a higher death rate with high dose clarithromycin • WHAT IS THE TAKE HOME FROM THIS? • In my view, the outcomes should be viewed separately

45. Surrogates & Antibiotic Development • First, the dose response should be evaluated to ascertain which clarithromycin dose is most effective • Second, a safety assessment should be done • Here, we would conclude that there were some, but marginal differences in the microbiological activity of high dose clarithromycin • However, on the safety evaluation, it had more deaths

46. Surrogates & Antibiotic Development • Conclusion: The loss of safety with high dose clarithromycin outweighed the minor increase in effect • Should a microbiological endpoint ALWAYS be used to measure effect?ANSWER: NO! • There are certain instances where there is a high cure rate with no therapy (Polyanna Effect) • Use of Time-To-Event analysis with measures of “How do you feel?” endpoints are reasonable

47. Surrogates & Antibiotic Development • We all know about sinusitis and AEBCB and (in kids) otitis media • Sujata Bhavnani, Paul Ambrose and I were recently VERY surprised by the analysis of a CAP trial

48. Surrogates & Antibiotic Development Note 70% probability of cure at zero drug exposure These patients were all Fine 1’s and 2’s

49. Surrogates & Antibiotic Development • In such circumstances, “is your cough or sinus pain or ear ache better?” is a perfectly reasonable approach tied to a time-to-event analysis • On the other hand, in patients with VAP with destroyed lung from Pseudomonas exotoxin A getting Ceph du jour, asking about feelings may be impeded by the ET tube and asking the antibiotic to regrow the lungs will be like Waiting for Godot AND IS NOT REASONABLE

50. Surrogates & Antibiotic Development • We have new tools and better understanding that allows better, more scientific inferences to be drawn without exaggerating risk • There are times when evaluation of “how do you feel?” is the most appropriate approach (and makes a heck of a lot more sense than waiting 10-14 days after the sentinel event takes place to make the evaluation) • In sick patients, the microbiological endpoint, particularly with resampling makes more sense