A Statistician’s Perspective on Biomarkers in Drug Development PSI Biomarkers Special Interest Group Martin Jenkins and Chris Harbron, AstraZeneca Co-authors:

A Statistician’s Perspective on Biomarkers in Drug Development

PSI Biomarkers Special Interest GroupMartin Jenkins and Chris Harbron, AstraZeneca

Co-authors: Aiden Flynn, Trevor Smart, Chris Harbron, Tony Sabin, Jayantha Ratnayake, Paul Delmar, Athula Herath, Philip Jarvis and James Matcham;

Volume 10, Issue 6, Pages 494-507, November/December 2011

What is a biomarker?

• Many endpoints could be considered as biomarkers• Commonly thought of in terms of biological / tissue samples (eg

blood), imaging techniques or even examinations• Many technology platforms (proteomics, histopathology etc)• Does not define one purpose and so a clear objective and

terminology can be helpful• Appear at many stages throughout the development program

2PSI Biomarker SIG27/09/2012

Biological marker (biomarker): A characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. NIH biomarker definitions working group, 2001

Types of biomarker


Biomarker Examples


Biomarker Current Use Classification Qualification

HER2, EGFR, K-RAS mutations

Directing treatment in oncology

Predictive biomarker Defines indication in label, diagnostic development

UGT1A1, TMPT polymorphisms

Predisposition to certain toxicities

Predictive biomarker Appear in label as risk factor suggesting dose adjustment

AB1-42 Diagnose prodromal Alzheimer’s Disease

Prognostic biomarker Enrich trial populations. Example of qualification

Gene signature chips

Prognosis prediction in oncology

Prognostic biomarker Diagnostic qualification process applies

CRP, IL-6, TNFa in blood samples

Proof of principle in inflammatory diseases

Pharmacodynamic biomarker

Fit for purpose assay validation

FDG-PET imaging Proof of concept (eg Tumour metabolism)

Pharmacodynamic biomarker

Collaborative opportunities for qualification

HbA1c Represents glycemic control in diabetics

Surrogate Endpoint Primary evidence for label. Qualification burden

What characteristics are we interested in?• It is generally advisable to learn as much as possible about a

biomarker prior to it’s application for decision-making– The science behind the choice of biomarker should be supported prior

to initiating studies– The practical feasibility of using the marker should be examined– The statistical properties of the biomarker are of interest

• Statistical properties of interest include– Variability estimates (and components of variability, within and

between subjects)– Effect sizes (using a positive control, challenge model or other data

sources) and dynamic range– Distributions and scaling approaches required– Appropriate analysis methods considered


Methodology studies• Feasibility / Methodology studies can be used to learn the characteristics

of an endpoint, but also the practicalities– Feasibility of multiple assessments – Acceptability to the patient, degree of dropout

• The same design and analysis methodology should be used as is envisaged for the subsequent clinical trial

• Information learned can aid in designing or sizing this trial• Work carried out should be fit-for-purpose enough to ensure that there

is sufficient confidence that decisions can be made based upon the biomarker, given it’s intended use. Further studies, reviews or meta-analyses can aid in this if required.


Sources of variation

Much biomarker work is accompanied by similar practical challenges around controlling sources of variation:•Patient-related factors

• Individual biological factors: genetics, race, age, comorbidities• Social and habitual factors: smoking, physical fitness, diet• Natural biological variation: eg diurnal effects

•External factors:• Pre-analytical variation: sample collection

and fixation practices• Analytical variation: between laboratories,

readers and batches, technical precision of the assay


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Sources of biasBias can arise, especially in open-label studies, for several reasons:•Verification bias because of the choice of locally available methods•Patient selection bias, for example, patients with breast cancer family history, may not consent to BRCA DNA testing •Treatment allocation bias, if treatment assignment is based on a subjective assessment, as in some histo-pathological endpoints

• Being asked the same question repeatedly, for example in a pain challenge model, could induce false differences• If a scoring method is subjective, then measures should be taken to minimize bias, for example, with the use of scripted questions, blinding or standard operating procedures.• Conclusions may be subject to many caveats if they are not based upon complete and representative data-sets


Sources of missing dataBiomarker studies can suffer from missing data issues, especially in exploratory situations:•Inaccessibility of tissues (e.g. lung cancer)•Low consent rates for optional samples•Lack of residual tissues in complete responders•Poor quality of fixation in archival samples•Patient drop-outs (lack of response or toxicity)•Poor data handling procedures cf. clinical data

Likely sources of missing data should be considered in advance so as to minimise potential implications and make appropriate assumptions


Multiplicity when considering many markers• Range of new high-dimensional technologies

– e.g. genetics, genomics, proteomics, metabonomics, NGS• Allow understanding in detail at a molecular level the processes of

disease and response to treatment and identify biomarkers that can identify or predict these changes.

• Multiplicity becomes a concern and requires new approaches to be adopted– e.g. False Discovery Rate, Permutation testing, Significance Analysis of

Microarrays (SAM)• Pre-analysis filtering of variables can help• Want to emerge with both statistical significance &

scientifically relevant and meaningful effect.– 2D FDR


Composites and Multivariate Analyses• Visualisation key

– Principal Components Analysis (PCA), Clustering• Wide variety of supervised multivariate predictive modeling

techniques are available– Regression-based approaches e.g. Partial least Squares (PLS), Elastic Nets– Proximity-based methods e.g. Nearest Neighbours– Tree-based methods e.g. Random Forests, Gradient Boosting– Distance-based approaches e.g. Support Vector Machines (SVM)

• Not just “Black-Box”, Interpretation important– Importance scores

• Study design is critical, as is visualization, understanding data quality and its impact on subsequent analyses.– MAQC-II


Biomarkers for Personalised Healthcare• Personalized health care offers the potential to identify patients

more likely to derive benefit from treatment and as such is of great interest to Pharmaceutical companies, Regulatory Authorities and Health care Providers

• Predictive markers – marker by treatment interaction• Gives rise to new set of challenges : eg. Low power to robustly

identify biomarkers in exploratory studies• Developing from a Biomarker to a Companion Diagnostic

• Understanding and quantifying the factors that may impact the performance of the diagnostic

• Identifying an optimal cut-off• Additional regulatory process


Study design options• All-comers design

– Subjects enrolled into groups, and retrospectively measured

• Stratification design– Biomarker status at screening used

as a stratification factor in randomisation to ensure balance


• Targeted design– Only biomarker positive patients recruited into study

• Enriched design– Hybrid, recruiting a limited number of biomarker negative patients

with a greater representation of biomarker positives

• Adaptive design– Many options allowing testing and refinement of a biomarker

Safety biomarkers preclinical & translation

• Predictive safety biomarkers allow early detection of toxicity and assessment of human risk

• Preclinical qualification including mechanistic understanding of the relationship between the biomarker and organ damage

• Use known organ toxicants• Determination of organ toxicity in rat by qualified toxicological

pathologist• Understanding properties of translation to man• Animal Model Framework (AMF) project is a collaborative effort

combining pre-clinical and clinical data to determine operating characteristics and refine thresholds of Safety Pharmacology models


Safety biomarkers - clinical

• Clinical qualification less straightforward because generally verification of organ toxicity not possible

• Utilise methods that can assess performance in absence of gold standard including Bayesian approaches

• Currently lab parameters used to indicate kidney and liver injury, but poor performance– e.g. by time serum creatinine indicates drug induced kidney injury, a

high degree of kidney function loss has already occurred • Public-private precompetitive partnerships established for

searching for, validating and qualifying safety biomarkers for predicting drug-induced organ injury– Predictive Safety Testing Consortium (C-Path), SAFE-T (IMI)


Biomarker qualification• Formal biomarker qualification process have been introduced:

– FDA (qualification process for drug development tools) – EMA (qualification of novel methodologies and biomarkers)– Examples on EMA and FDA websites

• These work alongside usual scientific advice routes and are a way to seek regulatory opinion on acceptability of a marker for a given use

• This is not mandatory and so is usually not required, but may be an advantageous step, particularly for a marker with wide applicability (For example for collaborative groups seeking to develop a new endpoint, particularly for toxicity)

• Diagnostic biomarkers would need to follow the in-vitro diagnostic regulatory processes


Surrogate endpoints• Those outcomes for which treatment effects correlate well with

those for an accepted clinical outcome at an individual and group level could potentially substitute for a recognized clinical endpoint

• Should lead to the same decision being made as if the clinical outcome had been used

• Examples exist (eg in diabetes or HIV), but a large body of evidence is required and this is not required for many purposes

• Surrogacy is often an unrealistic target. • Statistical methods have been developed to examine the association

between endpoints, but these can also be applied to other situations, for example when considering assurance when endpoints differ between studies


Future challenges


Issues to address in the future

i How can the traditional clinical development program be reengineered, so allow for greater learning around a biomarker without delaying the drug program?

Ii What study designs can be used to translate markers of drug toxicity into man given that the pre-clinical studies used for validation are not feasible to conduct in humans?

iii Many technologies suffer from a ‘batch effect’. What pre-processing techniques can be developed to ensure we are measuring true biology rather than sample quality?

iv New techniques have moved the degree of dimensionality by several orders of magnitude. How, both practically and analytically, can we cope with this deluge of data?

v Effective ways, possibly Bayesian, could be developed to build existing pathway knowledge into data analyses.

vi When identifying biomarkers for PHC, interaction analyses typically have low power, especially with many potential markers. How can such markers be reliably identified?

vii As PHC becomes the processes for gaining approval of a drug, diagnostic and biomarker qualification may become more burdensome. How can this evidence be streamlined?

Conclusions & Recommendations


Key considerations for statisticians in biomarker development

i Define clear objectives shaped by the potential future use of the marker- This will clarify what can reasonably be claimed based upon the research- Validation work and study designs should be “fit-for-purpose” relative to these aims

ii Prospectively plan studies even if they are exploratory- Defining what represents “success” in advance will aid objective decision-making

iii Consult expert colleagues to understand and shape the endpoints definitions- Learn about distributions and pre-processing- Influence scoring methods so as to get maximum information

iv Gather prior knowledge on sources of variability and causes of missing data - Plan ahead to avoid sample and data management issues rather than being reactive- Adjust missing data assumptions accordingly

v Learn about a new marker using methodology studies or translational science- Estimates of variability or effect size can be valuable if reflective of situation of use

Conclusions & Recommendations


Key considerations for statisticians in biomarker development

vi Analyse a biomarker using the appropriate methodology for the given endpoint type- As with other endpoints there is no universal approach

vii Be alert when using large numbers of candidate biomarkers- Particular attention should be paid to issues of multiplicity and model validation

viii Maintain a sceptical and questioning point of view to manage expectations- Consider results in relation to prior knowledge and study aims, avoiding extrapolation

ix Consider how to add confidence to findings using other sources of information- Integrate biological information as well as numerical results

x Remember that there is nothing mysterious about biomarkers- A consideration of key statistical principles can be instructive in most situations

Please get in touch if you would like to get involved!• Join the mailing list or linked-in group in• Help on the SIG committee• Review papers, training, discussion groups…• Attend one of our free meetings /or our conference sessions

• Go to www.psiweb.org and click on “committees and sigs” in the menu to find our webpage with more information

PSI Biomarker Special Interest Group


Biomarker Validation - Case Studies and Approaches MedImmune, Cambridge, 10 October 2012

http://www.psiweb.org/

A Statistician’s Perspective on Biomarkers in Drug Development PSI Biomarkers Special Interest Group Martin Jenkins and Chris Harbron, AstraZeneca Co-authors:

Documents

biomarker examples

types of biomarker

choice of biomarker

oncology predictive

nih biomarker definitions

qualification burden

analysis methodology

chris harbron