Quiet Selection Biomarkers in Drug Development: An initial move towards individualized treatment Michael Ostland Genent.


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Quiet Determination Biomarkers in Medication Improvement: An initial move towards individualized treatment Michael Ostland Genentech BioOncology April 21, 2005. Diagram. Foundation A few Difficulties of a Medication Improvement Program that incorporates Biomarkers Choice Making Logistical and Specialized
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Persistent Selection Biomarkers in Drug Development: An initial move towards individualized treatment Michael Ostland Genentech BioOncology April 21, 2005

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Outline Background Some Challenges of a Drug Development Program that incorporates Biomarkers Decision Making Logistical and Technical Wide-scale Screening of Potential Biomarkers Examples Discussion

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Background Very harsh rundown of "ordinary" medication development* :

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Background (2) Considerable between patient changeability in treatment advantage is the standard. For some profoundly powerful medications, numerous patients won\'t profit at all There are a few cases of medications/signs where some of this fluctuation is record for: Study of clinical prognostic components Study of clinical prescient variables PK contrasts from C-P450 chemicals Drugs focused to a patient sub-populace characterized by a particular sub-atomic biomarker (regularly identified with the MOA of the medication)

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What is a Biomarker? A biomarker is a trademark that is impartially measured and assessed as a marker of ordinary biologic procedures, pathogenic procedures, or pharmacologic reactions to a remedial intercession (Biomarkers Definitions Working Group). Our advantage is in the distinguishing proof of benchmark measured biomarkers that might be prescient of clinical advantage taking after resulting treatment .

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Background (4) Identification of sub-atomic biomarkers may permit medications to be focused to treat those patients who will profit most from the treatment. Expanded advantage could originate from expanded viability and additionally diminished poisonous quality.

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Background (5) "Individualized treatment" has gigantic ramifications for the medicinal services framework and pharmaceutical commercial center Less experimentation in endorsing Smaller pool of qualified patients Greater advantage in these patients could mean Great upper hand Easier to get re-imbursement from guarantors

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Challenges Decision Making Logistical and Technical Screening Potential Biomarkers

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Decision Making Phase II trials are utilized to get an early sign of adequacy, further evaluate wellbeing, and distinguish a promising dosing regimen. The general target is (as a rule) to permit a choice whether to continue into protracted and exorbitant stage III trials to affirm viability. Evaluating a potential biomarker adds another layer of intricacy to the basic leadership handle.

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Typical Oncology Randomized Phase II Trial Chemo + Placebo Compare wellbeing and viability (more often than not tumor reaction rate or time to infection movement) among the three arms. Selected Patients randomize Chemo + low dosage sedate Chemo + high measurement medicate Design the review so we are probably going to have satisfactory data to pick among three conceivable choices: Proceed to stage III with low measurements tranquilize Proceed to stage III with high measurements sedate Do not continue to stage III as of now

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Decisions with a Biomarker There are seven conceivable choices about Phase III when measurement and biomarker inquiries are a piece of the improvement

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Positive examine Negative Indeterminate Positive test Negative Indeterminate Phase II Trial w/Biomarker & Dose Design with review Dx testing Chemo + Placebo Enrolled Patients randomize Chemo + low measurement Chemo + high measurement

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Logistical & Technical Issues Identification of a potential biomarker Timely advancement and specialized approval of an industrially reasonable test ("Dx test") Acquiring usable patient tissues with appropriate educated assent from clinical trials Dealing with uncertain test comes about Clinical approval of the prescient estimation of the Dx test, including CDRH directions.

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Wide-scale Screening of Potential Biomarkers Technologies, for example, DNA microarrays permit concurrent measuring of thousands of potential biomarkers. Approach : Assay tests from a randomized clinical trial; recognize markers where measure reaction is related with treatment advantage. Inspect practical information on the distinguished markers Test few the most encouraging markers in a moment trial (in a perfect world tentatively)

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Screening Details Rank all markers utilizing a factual model that models clinical result as a component of every marker (each one in turn), treatment assemble, and other clinical covariates (if proper). Decide a remove that records for testing changeability & assortment Estimate the extent of the relationship amongst biomarker and treatment impact Work with Bioinformatics gathering to translate, refine, and repeat.

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Screening Details (2) Account for assortment utilizing a False Discovery Rate(FDR) controlling technique (Benjamini & Hochberg; Story). FDR is characterized as the normal extent of rejected theories that are erroneously dismisses ("dishonestly found"). Less preservationist than FWER controlling systems, so might be more fitting for theory era.

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Screening Example 60 patients with stubborn ovarian malignancy were randomized similarly to standard chemo with or without test medicate X. 42 had usable tissue tests that were keep running on Affymetrix Microarrays with ~40K mRNA tests. Essential endpoint was term of PFS Prior reaction to first line regimen of platinum-based chemotherapy is a known prognostic variable

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Ranking Genes For each of J biomarkers, fit a univariate Cox-PH demonstrate. Let speak to the danger work for the kth quiet in the model for the jth biomarker: where T k is treatment marker, X kj is the (log) expression measure for the jth quality in the kth subject, and greek letters are obscure parameters. Z k is a pointer that the kth subject was known to be impervious to platinum-based chemotherapy at the season of randomization.

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Ranking Genes (2) Let T j be the typical Wald test measurement of Following the advancement of Dudoit et al (2003), compute unadjusted p-values, p j , with a stage method. At that point compute balanced p-values Where r 1 ,… , r J is an arrangement that puts the unadjusted p-values in climbing request. At that point choosing qualities with gives solid control of the FDR (Benjamini and Hochberg, 1995)

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Results 72 qualities distinguished at the 10% FDR Bioinformatics examination & follow-up progressing Modeling and legitimate understanding require joint effort between clinical researchers, analysts, and bioinformaticians

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Example 2: Tarceva Lung A Drug w/Biomarker Information in Label

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Tarceva Phase III Study BR.21 in NSCLC BR.21 (NCI Canada, OSIP): Tarceva monotherapy versus fake treatment in chemotherapy-backslid (2 nd/3 rd line) NSCLC The essential endpoint was survival. Auxiliary endpoints were tumor reaction, tumor reaction length, movement free survival, QoL, and to connect the declaration of EGFR levels with results. 731 patients were randomized 2:1 to Tarceva or fake treatment. Intended to distinguish a 33% change in general survival with 90% power

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BR21: All patients 1.00 Tarceva Median = 6.7 mo (n=488) Placebo Median = 4.7 mo (n=243) Total N=731 0.75 1-yr Survival = 31% 1-yr Survival = 21% 0.50 Survival Distribution Function 0.25 0.00 0 5 10 15 20 25 30 Survival Time (Months)

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EGFR IHC and advantage from EGFR TKI Rationale: Tumors which express target ought to probably react than tumors which don\'t Does the examine really recognize subgroups with differential advantage? Will tumors which "don\'t express the objective" advantage from treatment?

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BR.21 Survival by EGFR IHC status (information from 33% of patients) EGFR values utilizing "nearness of recoloring in at least 10% cells" as positive/negative cut point - 53% were EGFR positive EGFR IHC (+) survived altogether longer when treated with Tarceva versus fake treatment in BR.21 EGFR (- ) demonstrated no clear survival advantage with treatment in BR.21 however certainty interim for the EGFR(- ) subset is wide.

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Resulting Label Content on EGFR IHC Relation of Results to EGFR Protein Expression Status (as Determined by Immunohistochemistry) Analysis of the effect of EGFR expression status on the treatment impact on clinical result is constrained in light of the fact that EGFR status is known for just 238 review patients (33%) . EGFR status was discovered for patients who as of now had tissue tests preceding review enlistment. In any case, the survival in the EGFR tried populace, and the impact of TARCEVA were practically indistinguishable to that in the whole review populace, proposing that the tried populace was a delegate test. A positive EGFR expression status was characterized as having no less than 10% of cells recoloring for EGFR as opposed to the 1% cut-off determined in the DAKO EGFR pharmDx™ pack guidelines. The utilization of the pharmDx pack has not been approved for use in non-little cell lung malignancy . TARCEVA delayed survival in the EGFR positive subgroup (N = 127; HR = 0.65; 95% CI = 0.43 — 0.97) ( Figure 3 ) and the subgroup whose EGFR status was unmeasured (N = 493; HR = 0.76; 95% CI = 0.61 — 0.93) ( Figure 5 ), however did not seem to affect survival in the EGFR negative subgroup (N = 111; HR = 1.01; 95% CI = 0.65 — 1.57) ( Figure 4 ). In any case, the certainty interims for the EGFR positive, negative and unmeasured subgroups are wide and cover, so that a survival advantage because of TARCEVA in the EGFR negative subgroup can\'t be avoided.

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Figure 3: Survival in EGFR Positive Patients

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Figure 4: Survival in EGFR Negative Patients

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Figure 5: Survival in EGFR Unmeasured Patients

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Conclusions Patient choice by means of biomarkers guarantees to reshape the business and patient care There are intriguing and testing issues for medication improvement with a Dx test, and analysts will play an impor

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