Biomarkers can be used at various stages of drug development to help evaluate safety, efficacy, and mechanisms of action. They are classified based on what aspect of disease or drug effect they measure. Valid biomarkers are accurate, specific, modifiable by treatment, and predictive of clinical outcomes. Biomarkers help accelerate drug development by serving as surrogate endpoints that can predict clinical benefit faster than traditional endpoints. They can also help identify likely responders through personalized medicine approaches. Companion diagnostics developed alongside new drugs can help ensure the right patients receive the right treatment.

1. BIOMARKERS
By Dr Rajesh Mailagire (junior resident)
Guide – Dr R.S. Hiray
This Photo by Unknown Author is licensed under CC BY-NC

2. Contents
• Introduction of biomarker
• Definition of biomarker
• Classification and types
• Sources of biomarkers
• Surrogate end points
• Characteristics of Valid biomarkers
• Role of biomarkers in drug development
• Classes of biomarkers in clinical trials

3. Introduction
• The use of biomarkers in basic and clinical research as well as in
clinical practice has become so commonplace that their presence as
primary endpoints in clinical trials is now accepted almost without
question.
• In the case of specific biomarkers that have been well characterized
and repeatedly shown to correctly predict relevant clinical outcomes
across a variety of treatments and populations, this use is entirely
justified and appropriate.
• In many cases, however, the “validity” of biomarkers is assumed
where, in fact, it should continue to be evaluated and reevaluated.

4. Definition
• The Biomarkers, EndpointS and other Tools (BEST) glossary
defines a biomarker as a characteristic that is measured as an
indicator of normal biological processes, pathogenic processes,
or responses to an exposure or intervention, including
therapeutic interventions.
• Molecular, histologic, radiographic, or physiologic
characteristics are types of biomarkers.

5. Classification
• Type 0 - natural history of disease & correlate longitudinally with known
clinical indices, such as symptoms over the full range of disease states
eg. CRP
• Type 1 - intervention/drug activity markers
eg. Blood glucose lowering after antidiabetic drugs,
• Type 2 - Surrogate endpoints ( as change in that marker predicts clinical
benefit)
eg. Death from the heart disease is the endpoint of interest, but
cholesterol is the surrogate marker

6. Types
Based on disease
state
Based on biomolecule Based on other
criteria
1) Prediction marker 1) DNA marker, gene 1) Imaging marker
2) Detection marker 2) RNA marker 2) Pathological marker
3) Diagnosis marker 3) Protein marker-
enzyme
3) In silico marker
4) Prognostic marker 4) Carbohydrate
marker
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7. Sources of biomarkers
Biomarkers
Electrophysiological
Physiological
Histological
Cytological
Imaging
Laboratory
tests

8. Surrogate endpoints
• Definition: is a laboratory measurements or
physical sign used in therapeutic trials as a
substitute for a clinical endpoint and is a direct
measure of how a patient feels, functions or
survives.
• Special class of biomarker
• Changes induced by a therapy on a surrogate
endpoint are expected to reflect changes in a
clinical endpoints.

9. • Eg. Statins reduces cholesterol, without showing directly that statins
prevents death (elevated cholesterol levels increases heart disease)
clinical patients outcome – clinical endpoints
cholesterol – surrogate marker
• Expected to predict clinical benefit or harm based on epidemiological,
pathophysiological, therapeutic or other evidences
Eg : 1) blood pressure in stroke prevention
2) QT interval in drug safety
3) plasma glucose in diabetes

10. Biomarkers as surrogate endpoints- possible relationships
Types of relationship Value of biomarker Example
A. Unreliable interaction between
biomarker and the treatment
intervention
Biomarker of no value as a surrogate
endpoints
PSA is a useful biomarker for prostate
cancer detection but unreliable as an
indicator of treatment response.
B. The full effect of the intervention is
observed through the biomarker
assessment
Biomarker is an surrogate endpoint ---------
C. Intervention affects the endpoint and
marker independently
Biomarker has some value as a surrogate
endpoints
HIV antivirals – CD4 count as a biomarker,
with mortality as endpoint
D. Intervention has favourable effect on
marker but has no or unfavourable
effects on disease or well state
Biomarker is of little practical use as a
surrogate endpoint.
PVCs as a biomarker of fatal arrythmias
following myocardial infarction.

11. Characteristics of a Valid biomarker
• Safe and easy to measure
• Cost efficient to follow up
• Have great sensitivity, specificity, accuracy
• High predictive value
• Modifiable with treatment

12. Uses of biomarkers
• In clinical trials for drug development
• In screening
• For risk assessment ( biomarker of exposure)
• For diagnosis
- staging
- grading
- selection of initial therapy
- monitor recurrent disease
• For monitor therapy
• In selection of additional therapy

13. Role of biomarkers in drug development
• At present in drug development, researchers facing issues regarding
 requiring huge amount of money, time, patient.
 Attrition rate for drug in clinical development is high
 many drug withdrawn from market due to serious toxicities and
suffering after their approval.

14. Biomarker can:
• Monitor the safety of a therapy
• Determine if a treatment is having the
desired effect on the body
• Predict patients who might respond better
to a drug from a safety or efficacy
perspective
• Potentially enable time and cost savings in
clinical trial

15. • BIOMARKERS USED AS OUTCOMES
Conventional approach:
- Measures performance of novel therapies using clinical outcomes,
such as mortality or disease progression.
- Accruing enough information for clinical endpoints may take many
years.
 Biomarker-driven approach:
- Biomarkers can sometimes predict drug efficacy more quickly than
conventional clinical endpoints.
- Potential to accelerate product development in certain disease
areas.

17. Biomarkers in clinical research and drug
development
• Biomarkers may offer information about the
 mechanism of action of the drug
 efficacy
 safety/toxicity
 metabolic profile
• dose selection
• Evaluation of dose response
• Personalized medicine approach
• Identifying earlier those candidates that are likely to fail, they reduce drug
development costs, giving life to the concept of fail early , fail cheap

18. Biomarkers for efficacy and safety of drug
Eg.
• Name of drug – rosuvastatin
• Clinical end point – cardiovascular morbidity/mortality/cured
• Population – apparently healty with low LDL and high CRP
• Biomarker – CRP
• Result – drug effectively improves the prognosis of individuals with
high CRP levels
• Conclusion – CRP offers a new way of measuring the efficacy of
statins.

19. Biomarkers for mechanism of action
• Mechanistic biomarkers/ target biomarkers
• These biomarkers can be used to drive critical ‘go/no go’
decision in drug development
• To measure pharmacological effect of a drug
– Whether the drug interacts with its receptor (enzyme, protein)
– Whether it is distributed to the site where it needs to act
– Whether there is some form of downstream pharmacology
– The dose ranges in which the drug is pharmacologically active.

20. • Mechanistic biomarker- aldosterone(retain sodium and water)
• Drugs-5-HT4 receptor agonists (cisapride, mosapride)
• Disease- GERD
• Aldosterone secretion is a side effect of these drugs
• So aldosterone biomarker can assess whether novel 5-HT4 agonists
in development have a pharmacological effect
• Aldosterone can also be used to assess at what doses the 5-HT4
agonists have an effect.

22. • Biomarker Identification technologies
High throughput technologies
A. Genomics
1. Genome sequencing
2. Genome variation
3. Genome annotation
B. Transcriptomics
1. Microarrays
2. Gene expression data
C. Proteomics
1. Y2H method
2. Mass spectrometry
3. Protein chips
D. Metabolomics
1. NMR
2. Mass spectrometry
E. Other technologies-
1. Fluorescent indicators
2. Lab-on-chip
3. Nuclear magnetic
resonance
4. Mass spectrometry/liquid
chromatography
5. Nanobiotechnology
F. Imaging

23. A. Safety biomarkers
1. Liver safety tests
2. Renal safety tests
3. Haematology safety biomarkers
4. Bone safety biomarkers
5. Basic metabolic safety
biomarkers
6. Other specific safety biomarkers
Classes of biomarkers in Clinical trials
B. Efficacy biomarkers
1. Surrogate biomarkers
2. Predictive biomarkers
a. In personalized medicine
b. Personalized medicine and
companion diagnostics (CDx)
3. Prognostic biomarkers
4. Pharmacodynamic (PD) biomarkers

24. Biomarkers of liver toxicity
Cholestatic injury Cytotoxic injury Altered hepatic function
Alkaline Phosphatase
[AP, ALP]
Aspartate
aminotransferase [AST]
Creatine phosphokinase
[CPK]
5’ Nucleotidase ‐
[5-NT]
Lactate Dehydrogenase
[LDH]
Choline Esterase [ChE]
(acetylcholine esterase
and butyrylcholine esterase)
G Glutamyl Transpeptidase
[GGT]
Alanine aminotransferase
[ALT]
Decreased dye
clearance
•Sulfobromophthalein
•Indocyanine green
Total Serum Bile Acids Ornithine carbamyl
transferase [OCT]
Plasma Bilirubin Alanine Aminotransferase

25. Biomarkers for kidney toxicity
Serum Indicators Urine Indicators Urine Indicators
BUN Physical characteristics Chemical Characteristics
Blood creatinine Color/turbidity (RBC’s, bilirubin) Urinary protein –
tubular (low MW) or
glomerular (high MW)
Volume Urinary glucose –
no elevation of blood
glucose but glucosuria
(tubular)
Osmolality Urinary brush border enzymes
(ALP, AST, GGT)

26. Haematology safety biomarkers
• Bone marrow:- Primary target
• Effect:- changes in peripheral blood components.
• • Complete blood count :-
i. Total haemoglobin
ii. Haematocrit
iii. Red cell count
iv. Mean red cell volume,
v. Mean cell haemoglobin
vi. Red cell distribution width%
vii. Mean cell haemoglobin concentration
viii. Total white cell count
ix. Differential white cell count (Neutrophils, lymphocytes, basophils, Eosinophils, and
monocytes)
x. platelets

27. Bone safety biomarkers
• Living connective tissue
• Constantly under process of remodeling
• Includes bone resorption and formation.
• Bone biomarkers in clinical trials:- Serum calcium and inorganic
phosphates

28. Basic metabolic safety biomarkers
a) Blood glucose
b) Triglycerides (TG)
c) Total cholesterol
d) Low density lipoprotein cholesterol (LDLc)
e) High density lipoprotein cholesterol (HDL-c).
– are commonly used within the safety biomarker panel
but can be used as efficacy biomarkers too.

29. Other specific safety biomarkers
a) Serum immunoglobulin levels, C-reactive protein (CRP),
fibrinogen
b) Thyroid stimulating hormone (TSH), thyroxine, testosterone,
insulin
c) Lactate dehydrogenase (LDH), Creatine kinase (CK) and its
isoenzymes, cardiac troponin (cTn), and methaemoglobin

30. Efficacy biomarkers
• Purpose differs fundamentally from safety monitoring
• Used to demonstrate a change in all, or at least a good proportion of
treated subjects
• The more positive the biomarker, the higher the efficacy of a drug.

31. Predictive biomarkers
• Stratify patient populations into responders and non-responders
• Predict whether or not a drug will have the intended effect
• Or forecast the extent to which a drug can be effective and/or toxic in
different patient populations.
• Mahgoub et al, 1977 and Tucker et al, 1977:-
The discovery of Cytochrome P450-2D6 (CYP2D6) polymorphism in
1977 opened the door for research on the impact of such metabolizing
enzyme’s genetic variability on the efficacy and toxicity of drugs.

32. • 76 genetic and genomic biomarkers (CYP2D6,CYP2C19):-
on FDA labels of 70 approved drugs – oncology, psychiatry,
antiviral and cardiovascular drugs

33. Drug label information on genomic biomarkers :-
1) Describe drug exposure and clinical response variability
2) Risk for adverse events
3) Genotype specific dosing
4) Mechanisms of drug action
5) Polymorphic drug target and disposition genes
6) Precautions- interactions, contraindications, patient
counseling, nutritional management

34. Predictive biomarkers
• Human genome project :- understanding of human
genetics and the associated biology
• Patients with different genetic makeup manifest diseases
differently and respond to medication differently – in terms of
both efficacy and safety.
• “Right population , right drugs, right
time, right dose, right price”
• Potential for mitigating the problem of universalizing
therapy into a single, all-encompassing solution.

35. PERSONALIZED MEDICINE AND COMPANION DIAGNOSTICS (CDX)
• Recent advances in cancer research :- focused on drug candidates with specific
molecular targets including mutated
genes in cancer cells.
• In-vitro diagnostic test (IVD) - To achieve the greatest benefit from such types of
therapeutic agents
• IVD can be an existing test for a biomarker :- FDA as “known valid;”
Eg. LDL-c, HbA1c, and CYP2C19.
• Biomarker appears to have predictive value but not yet replicated or widely
accepted:- classified by the FDA as“probable valid,”
Eg. EGFR and KRAS mutations.

36. • This approach mandates co-development of an IVD with a drug- a
companion diagnostic (CDx).
• Co-development can occur during any stage of drug development
• OPTIMALLY - integrated early in the drug’s development program so that trial
data will support both drug and test approval.
• Clinical qualification :- prospective, retrospective path remains a possibility.
• Biomarker assay should be analytically validated before testing clinical
samples.

37. Drug Indication Biomarker/CDx
Imatinib CML BCR-ABL (PCR), c-KIT IHC
Erlotinib NSCLC, Pancreatic EGFR AND KRAS mutation
Gefitinib NSCLC EGFR AND KRAS mutation

38. Prognostic biomarkers
• Predict the risk or outcome of a disease in patient population without
the involvement of therapy.
Eg. a population that tested positive for a given prognostic biomarker
can survive longer or live better than another that tested negative.
• In addition to its predictive power, prognostic biomarkers mayhelp
enrich a clinical trial by choosing people more likely to respond to
treatment
• Prostatic specific antigen to predict survival in prostatic cancer patients
– (D’Amico et al, 2004 and Kelloff et al, 2004)

39. 1) Preoperative CA125 to predict metastatic disease in patients with uterine
carcinoma (Gupta et al, 2011)
2) CRP as a risk factor in cardiovascular events (Ridker et al, 2008 and Abd et
al, 2011)
3) CRP to predict reduced overall and disease-free survival breast cancer (
Allin et al, 2011)
4) Serum LDH to predict overall survival in metastatic brain tumors (Eigentler
et al, 2011).
5) The number of circulating tumor cells (CTC) was shown to predict overall
and progression-free survival in patients with metastatic breast and ovarian
cancers (FDA, 2005 and Poveda et al, 2011)
6) To predict the effect of treatment earlier than imaging (Nakamura et al,
2010).
7) HER2-positive CTC was suggested as a prognostic value in metastatic
breast cancer (Hayashi et al, 2011).

40. Pharmacodynamic biomarkers
• Demonstrate that a drug hits its target and impacts its biochemical
pathway.
• To demonstrate proof of the drug’s mechanism of action (POM), i.e.
markers of pharmacological response.
• Proof of concept (i.e., Does hitting the drug target result in the
desired biologic effect?).
• In correlation with pharmacokinetic (PK) measurements, help to
determine- effective dose and dose schedule.
• Constitutes the majority in early phases of drug discovery (preclinical,
phase I, and, probably, phase II).

41. • The contribution of biomarkers to the goals of phase I oncology trials was
analyzed
1. Supported the proposed mechanism of action in 39% of the trials
2. Contributed to dose selection for subsequent phase II studies in 13%
3. Contributed to the selection of dosing schedule for phase II studies in 8%
4. Potentially useful for selecting a patient population in subsequent studies
in 19% of the trials.

42. • These biomarkers were determined in
1. Serum (36.8% of total)
2. Tumor tissue (25.6%)
3. Peripheral blood mononuclear cells (22.7%)
4. Normal solid tissue (3.7%)
5. Cerebrospinal fluid (0.2%)
6. In addition to 10.9% by special in-vivo imaging.
• The non-imaging biomarkers included
• Proteins
• Cytokines
• Enzyme activity
in serum, CSF, or tissue lysates, proteins by
immunohistochemistry (IHC), and DNA and RNA gene expression

43. Conclusion
•Patients expect approved drugs that work, are safe and are “right” for
them.
•Biomarkers can help drug development focus more on defined
subgroups of patients, thereby potentially increasing treatment efficacy
and safety.
•Helps to make a decision to move to the next phase
•Offer strong supporting evidence and in the future will be the key data
in certain programs.
•Offer an objective, biological indicator, rather than just seeing whether
the patients feel better

44. • Biomarker enabled R&D is maturing into a new discipline that is
addressing these goals with more precision.
• However, the science is outpacing widespread acceptance.
• The path toward acceptance by regulators and the medical
community is through discovery and consistent validation of genomic,
proteomic, in vitro and imaging biomarkers.
• Further collaborative efforts and powerful technology approaches
can increase public confidence.