Biomarkers in Drug Development

• The role of biomarkers is expanding to inform decisions in all phases of drug development, from discovery and preclinical stages to clinical trials and post-marketing studies.
• High costs from drug failures during clinical trials have increased interest in biomarkers as biological indicators of factors like disease progression, therapeutic effects, and drug toxicity.
• Biomarkers help reduce drug attrition during preclinical and clinical phases, lowering overall development costs.

Biomarkers Defined

• Biological Marker (Biomarker): An objectively measured characteristic that indicates normal, pathogenic, or pharmacological responses to therapeutic intervention.
• Clinical Endpoint: A characteristic that reflects how a patient feels, functions, or how long they survive.
• Surrogate Endpoint: A biomarker used in place of a clinical endpoint, chosen based on scientific evidence to predict clinical benefit, harm, or lack of effect.

Ideal Biomarker

• Expression is significantly increased, especially in related disease conditions.
• Readily quantifiable in accessible biological fluids or clinical samples.
• Shown to correlate with an interested outcome progression.
• Economical, quick, and consistent.

High-Throughput Technology Platforms for Biomarker Discovery

• Genomics: Genome sequencing, variation, and annotation.
• Transcriptomics: Microarrays and gene expression data.
• Proteomics: Mass spectrometry and protein chips.
• Metabolomics: NMR and mass spectrometry.

Biomarker Evolution

• Biomarker Identification: Identify candidate markers.
• Exploration: Research and development tool, find best candidates and best assays.
• Demonstration: Define assay performance (sensitivity + specificity + reproducibility).
• Characterization: Known or established biomarker quality in defined clinical populations.
• Qualification: Biomarker can substitute for a clinical endpoint. Acceptance by regulators.
• Diagnostic: General medical and research use.

Types of Biomarkers

• Disease Related: Detect and stage disease, predict treatment response, monitor progression/recurrence and compliance, and determine treatment efficacy.
• Type 0: Natural History Marker
• Type 1: Biological Activity Marker
• Type 2: Single or Multiple Markers of Therapeutic Efficacy

Disease-Specific Biomarkers

• Diabetes Mellitus: RBS, FBS, HbA1c, retinal assessments
• Hypertension: BP, HR, plasma renin, angiotensin I/II, aldosterone
• Heart Failure: PRO-BNP
• Asthma/COPD: PFTs, leukotrienes
• Cardiac Ischemia: Troponins, myoglobins
• Cancer Markers: PSA, HER-2/Neu, EGFR
• Oxidative Biomarkers: MDA, hydrogen peroxide, α1-antiproteinase
• Antioxidant Biomarkers: SOD, glutathione, catalase

Types of Biomarkers Explained

• Type 0: Markers of a disease's natural history, correlate with clinical indices such as symptoms like CRP
• Type 1: Reflect effects of intervention according to the drug's mechanism, such as HbA1C
• Type 2: Surrogate endpoints where changes predict clinical benefit, such as LDL-C

Classes of Biomarkers in Clinical Trials

• Safety biomarkers
• Efficacy biomarkers
• Includes imaging (CT, MRI, PET, X-ray) or clinical laboratory testing

Safety Biomarkers

• Safety lab biomarkers can act as common vital organ function tests applied across different therapeutic areas or as specialized testing applied to detect unique toxicities.
• Careful test selection is essential during phases 1 and 2, based on compound profile and pre-clinical toxicology data.

Types of Safety Tests

• Liver Safety: AST, ALT, ALP, GGT, Bilirubin
• Renal Safety: BUN, Sr Creatinine, GFR
• Hematology Safety: Complete blood count
• Bone Safety: Calcium, Inorganic phosphates
• Basic Metabolic Safety: Glucose, Cholesterol, Uric acid

Efficacy Biomarkers

• Efficacy biomarkers show change in treated subjects.
• The more positive the biomarker, the higher the efficacy of the drug.
• Surrogate Biomarkers/Endpoints
• Predictive Biomarkers
• Pharmacodynamic Biomarkers
• Prognostic Biomarkers

Surrogate Endpoints

• Laboratory/physical measurements used to indicate drug response in clinical trials, replacing clinical endpoints.
• Surrogate biomarkers assess therapeutic agent benefit or harm.
• Useful for proof of concept even if evidence is insufficient for surrogacy.

Surrogate Biomarker Examples

• Hypertension: Blood pressure (surrogate) relates to stroke (clinical endpoint).
• Dyslipidemia: Cholesterol, LDL (surrogate) relates to coronary artery disease (clinical endpoint).
• Diabetes: Glycosylated hemoglobin (HbA1c) (surrogate) relates to retinopathy, nephropathy, neuropathy, heart disease (clinical endpoint).
• Glaucoma: Intraocular pressure (surrogate) relates to loss of vision (clinical endpoint).
• Cancer: Biomarkers tumor shrinkage, response rate (surrogate) relates to progression-free survival, overall survival (clinical endpoint).

Predictive Biomarkers

• Stratify patient populations into responders and non-responders.
• Predict whether or not a drug will have the intended effect.
• Forecast the extent to which a drug can be effective and/or toxic in different patient populations.

Predictive Biomarker Examples

• Imatinib (Drug): CML (Indication) - BCR-ABL (PCR), c-KIT (Biomarker)
• Erlotinib (Drug): NSCLC, pancreatic (Indication) - EGFR and KRAS mutation (Biomarker)
• Gefitinib (Drug): NSCLC (Indication) - EGFR and KRAS mutation (Biomarker)
• Trastuzumab (Drug): Breast cancer (Indication) - HER2 (Biomarker)

Pharmacodynamic (PD) Biomarkers

• These biomarkers demonstrate that a drug hits its target and impacts its biochemical pathway.
• Necessary to demonstrate proof of the drug's mechanism of action.
• This class of biomarkers constitutes the majority of biomarkers in early phases of drug discovery (preclinical, phase I, and phase II) and helps determine effective dose and dose schedule.
• Non-imaging biomarkers include proteins, cytokines, and enzyme activity in serum, CSF, or tissue lysates by immunohistochemistry (IHC), and DNA and RNA gene expression, like Ki67 in Ca Prostate.

Prognostic Biomarkers

• Prognostic biomarkers predict disease risk or outcome in patient population without therapy.
• They may enrich clinical trials by selecting people more likely to respond to treatment.
• Examples include prostatic specific antigen for survival in prostate cancer and CRP as a risk factor in cardiovascular events.

Phases of Evaluation of Biomarkers

• Identification should proceed systematically.
• The National Cancer Institute's 'Early Detection Research Network' developed a five-phase approach in 2002.
• Phase 1: Prioritize identified markers based on their diagnostic/prognostic/therapeutic value and their potential for routine clinical use.
• Phase 2: Establish an assay with clear clinical use, validated for reproducibility and portability across labs.
• Phase 3: Evaluate sensitivity and specificity for detecting diseases yet to be detected clinically.
• Phase 4: Evaluate the sensitivity and specificity of the test on a prospective cohort. Estimate false referral rate and describe extent/characteristics of the disease.
• Phase 5: Evaluate overall benefits and risks of the new diagnostic test on the screened population.

Biomarkers in Phase I Trials

• In phase I studies, pharmacodynamic biomarkers are often of interest.
• Modulation of these markers may support drug target inhibition and selection of drug and dose for further evaluation.
• These are almost always exploratory biomarkers.
• The goal is to provide evidence that the agent reaches or modulates the putative target.
• These studies can be conducted by analysis of samples and/or images obtained prior to and after treatment, or by comparison to an untreated control.

Biomarkers in Phase II Trials

• Provide evidence that the agent modulates the putative target or pathway in a pharmacodynamic assessment similar to the phase I setting
• Evaluate the association between the biomarker and clinical outcome
• Determine patient eligibility (for example, HER2 status for trastuzumab trials)
• Determine the dose-response relationship of a pharmacodynamic marker across a narrow set of dose cohorts (generally one or two) and more homogeneous patient population.

Validation of Biomarkers

• Statistical validation is required to justify biomarker use in clinical trial after selection based on biological plausibility and technical feasibility.
• Validation begins with demonstrating a correlation between the marker and endpoint, followed by independent statistical validation.
• Type 0 markers can be characterized in phase 0 studies using a reliable assay in a well-defined patient population for a specified time.
• Ideally, establish a linear (+ve or -ve) relationship with the gold standard clinical assessor.
• A priori validation of type I biomarkers is impossible for truly novel targets without an effective positive control treatment; for novel targets, biomarker validation runs parallel with the drug candidate.
• Type 2 biomarkers (or surrogate endpoints) must be relevant to both the drug's mechanism of action and the disease's pathophysiology.
• Changes in these biomarkers should reflect treatment benefit, necessitating effective therapy for validation.
• A phase-3 study supporting claims of innovator drug's effectiveness is sufficient to validate type 3 biomarkers.

Fit-For-Purpose Method Validation

• Practical approach of validating biomarkers.
• Fit-for-purpose method validation provides for efficient drug development by conserving resources in the exploratory stages of biomarker characterization.
• Describes distinct stages of the validation process including pre-validation, exploratory and advanced method validation, and in-study method validation
• Biomarker data must be reliable and accurate for decision making.
• Analytical validation requirements must be specific to the stage of drug development, and consideration given to both the intended use of the biomarker data and the related regulatory requirements.

Bioanalytical Validation vs. Biomarker Validation

• Assay Method: Quantitative (Bioanalytical), Quasi-quantitative (Biomarker)
• Regulatory Requirements: GLP (Bioanalytical), No specific guidelines (Biomarker)
• Nature of Analyte: Exogenous (Bioanalytical), Endogenous (Biomarker)
• Precision/Accuracy: Robust with acceptance criteria (Bioanalytical), Variable (Biomarker)
• Sensitivity: LLOQ defined by acceptance criteria (Bioanalytical), Limited sensitivity with dynamic range (Biomarker)
• Specificity: Drugs absent in sample (Bioanalytical), Biomarkers present in sample (Biomarker)
• QC: Certified standard available (Bioanalytical), Not usually available (Biomarker)

Design Considerations for Biomarker Studies

• The choice of an appropriate design for a trial will largely depend on:
  • The strength of existing evidence for a biomarker
  • The nature of conclusions to be drawn
  • The strength of evidence desired at the trial's conclusion
  • The available resources
• For prognostic biomarkers, retrospective studies using data from well-conducted clinical trials will be sufficient.
• In the case of predictive biomarkers, more rigorous standards must be met to justify their use in a clinical setting.
• Since predictive biomarkers seek to prospectively identify patients likely to have a favourable clinical outcome in response to targeted therapies, validation often may require comparing outcomes between biomarker-positive and biomarker-negative patients.
• Prospective, randomized controlled trials (RCTs) remain the best approach for establishing the clinical utility of predictive biomarkers.
• Yet traditional RCTs only allow for the estimation of the average treatment effect in the overall study population rather than in marker-defined subpopulations.
• Alternative trial designs like adaptive clinical trials need to be considered for the evaluation and application of biomarker-based therapies.
• If evidence suggests that the benefits of a treatment are limited to the biomarker-positive subpopulation, an enrichment design strategy, in which only biomarker-positive patients are enrolled, may be the appropriate choice.
• Because they require relatively small sample sizes to demonstrate safety and efficacy, enrichment strategies may improve trial efficiency.
• However, such designs may only allow for partial evaluation of the clinical validity of biomarkers since they do not provide information on the effects of treatment in biomarker-negative patients.

Biomarkers as Surrogate Endpoints

• Evaluation of biomarkers as surrogate endpoints is a challenging task.
• To be considered a surrogate, a biomarker must:
  • Correlate with the true clinical outcome.
  • Capture the full effect of treatment on the clinical endpoint.
• While the first criterion is relatively simple to demonstrate, not the second

Potential Uses of Biomarkers in Drug Development

• Target Discovery & Validation: Identify/justify targets for therapy; use Her 2 proto-oncogene as a marker of poor prognosis in breast cancer
• Lead Discovery & Optimization: Identify leads and evaluate molecularly targeted drugs in preclinical stages
• Preclinical Studies: Develop/validate animal disease models, assess toxicity and the safety of drugs
• Clinical Trials: Early evaluation of success/failure of drugs; rational selection of drug combinations; optimization of dose/schedule; identification of responders; development of new surrogate endpoints; predict clinical outcomes

Limitations of Biomarkers

• Expensive (cost for analyses)
• Storage (longevity of samples)
• Laboratory errors
• Normal range is difficult to establish
• The following are the major pitfalls in the translation from biomarker discovery to clinical utility:
  • Lack of making different selections before initiating the discovery phase.
  • Lack in biomarker characterisation/validation strategies.
  • Robustness of analysis techniques used in clinical trials.