Health & Medical Question

Do you think that pharmaceutical companies should routinely pursue orphan indications?  What are the major advantages and disadvantages of this strategy?

1. The landscape of clinical trials has changed drastically over the last decade. The
protocols are becoming more complex, leading to increased workload, burden on
patients, and longer treatment times. Additionally, there has been an increase in the
number of Phase 3 projects terminated, resulting in increasing costs of drug
development – which is an accumulation from preclinical studies, failures, salaries, and
the profit that could have been earned if the drug was approved.
2. The launch of ClinicalTrials.gov in 2000 provided key insight into the evolution of clinical
trials. With the requirement by ICMJE to report prospective registration of clinical trials
as a precondition for publication, this ensured that not only “successful” trials are
published. The details required on ClinicalTrials.gov have expanded overtime, such as
reporting results and adverse events. As of 2016, over 200,000 trials are listed and
conducted in 191 countries.
3. Phases of clinical drug development are categorized as I, II, III, and IV. Phase I studies
typically involve healthy volunteers or patients and conducted to determine responses
to drug in humans and animals. The goal is to assess safety, efficacy, tolerability, and
PK/PD of the drug. In Phase II trials, the drug is being studied in patients with the target
disease for the first time to assess dosing requirements and study efficacy. When the
drug development process fails, it typically happens in Phase II. Phase III studies are
done in a much larger scale to further establish safety and efficacy. Phase IV trials are
also known as Post Marketing Surveillance Trial, which may be required by regulatory
authorities or sponsors to monitor the safety of the new drug in large number of
patients.
4. A good protocol is designed to answer research questions. A good research question is
constructed with three key points in mind: feasibility, relevance, and justifiable costs.
Regarding feasibility, one must consider whether there is sufficient patient population
that can enroll in the study as well as whether the trial is manageable in scope. For
relevance, one must determine if the trial will advance scientific knowledge, guide
further research, or affect public policies. Lastly, is the study aligned with the
therapeutic focus of the sponsor to justify the costs it will take the conduct the study?
5. There are three ethical principles in clinical research that must be taken into
consideration. First, it is the principle of Respect for the Person. This principle has two
moral requirements – acknowledging that individuals should be treated as autonomous
beings capable of representing their own goals and interests and acknowledging that
people who are not capable of self-determination must be protected. Second,
beneficence notes that scientists must refrain from knowingly harming their research
subjects. Lastly, justice refers to the principle that the benefits and burdens of research
must be fairly distributed.
Pharmacology 526S
“Biomarkers and Their Role in Drug
Discovery and Development”
Mary Ann Pelleymounter, Ph.D.
Office of Translational Research, NINDS
National Institute of Health
Biomarkers and Their Role in Drug Development:
Topics for Today
• Why are Biomarkers Important?
• What is Translational Research and What is Translational
Medicine?
• Biomarkers and Their Role in Translational Medicine
• Types and Uses of Biomarkers in Discovery and
Development: Examples and Case Histories
• Identification, Validation, Qualification and FDA Approval
of Biomarkers or Diagnostics
• Biomarkers and the Future of Drug Development:
Precision Medicine
2
Why are Biomarkers Important?
Astra-Zeneca’s Experience (2005-2010)
Project Failures Due to Safety in Early Stages,
Due To Efficacy Issues in Later Stages
Translation to Human Biology
Is Important
Cook, D, Brown, D, Alexander, R, March, R, Morgan, P,
Satterthwaite, G, Pangalos MN (2014) Lessons learned
From the fate of Astra-Zeneca’s drug pipeline: a fiveDimensional framework, Nature Reviews Drug Discovery
13:419-431.
3
What is Translational Research?
• Definition:
– The process of applying ideas, insights and discoveries
generated through basic scientific inquiry to the
treatment or prevention of human disease*
*Funding Opportunity Announcemnt (2012) NINDS Cooperative Program in Translational Research (U01), PAR-13-022;
4
Translational Research Approach to Drug
Target Identification
“From Bench to Bedside and Back Again”
Fishburn CS (2013) Translational research: the changing landscape of drug discovery, Drug Discovery Today, 18:487-494.
5
What is Translational Medicine?
• Definition
– Research discipline designed to improve the predictability
of success for drug discovery and development
• Goals of Translational Medicine
– Improve the congruency of preclinical and clinical data
– Establish clinical proof of concept (efficacy and safety)
based on targeted mechanism of action
– Discover, validate and implement biomarkers to use along
with, or in lieu of clinical outcome endpoints
– Establish surrogate biomarkers to aid in early registration
of new drugs and to promote personalized medicine for
better patient selection
Day, M, Rutkowski JL, Feuerstein GZ (2009) Translational Medicine-a paradigm shift in modern drug discovery and development:
The role of biomarkers, Pharmaceutical Biotechnology, Eds Carols A. Guzman and Giora Z. Feuerstein, Landes Bioscience and Springer Science+Business Media
6
What is a Biomarker and How is it Different
from a Surrogate Marker or Clinical Endpoint?
• Biological Marker (Biomarker)
– An indicator of normal biological processes, pathogenic
processes or pharmacological responses to a therapeutic
agent
• Surrogate Marker
– Biomarker intended to serve as a substitute for a clinically
meaningful endpoint and is expected to predict the effect
of a therapeutic intervention
• Clinical Endpoint
– A clinically meaningful measure of how a patient feels,
functions or survives
Hunter DJ, Losina E, Guermazi A, Burstein D, Lassere MN, Kraus V (2010), A pathway and approach to biomarker validation and qualification for osteoarthritis clinical trials, 11(5):536-545.
7
Surrogate vs Clinical Endpoints:
Examples
Chau CH, Rixe O, McLeod H, Figg WD (2008) Validation of analytical methods for biomarkers employed in drug development, Clin Cancer Res, 14(19):5967-5976.
8
Role of Biomarkers in
Discovery and Development
• Indicator of disease state
– Correlate with disease initiation, progression, regression,
remission, relapse or modification
• Validate importance of target in disease and drug
development
• Define chemical-physical interaction of drug with target
• Define consequences of drug-target interaction
• Assist in patient selection
• Define likelihood of patients to respond to drug
Day, M, Rutkowski JL, Feuerstein GZ (2009) Translational Medicine-a paradigm shift in modern drug discovery and development:
The role of biomarkers, Pharmaceutical Biotechnology, Eds Carols A. Guzman and Giora Z. Feuerstein, Landes Bioscience and Springer Science+Business Media
9
Biomarkers as Indicators of Disease State
• Disease biomarkers must correlate statistically
with disease phenotype
– Correlation of levels or expression patterns in
tissue or bodily fluids should signify disease
initiation, progression, remission or relapse
• Examples (Good and not so great)
– Glycosylated hemoglobin in diabetes
– Positive symptoms in schizophrenia
Day, M, Rutkowski JL, Feuerstein GZ (2009) Translational Medicine-a paradigm shift in modern drug discovery and development:
The role of biomarkers, Pharmaceutical Biotechnology, Eds Carols A. Guzman and Giora Z. Feuerstein, Landes Bioscience and Springer Science+Business Media
10
Glycosylated Hemoglobin (HbA1c) as a Biomarker for Disease
State:Progression and Remission of Retinopathy
Conventional Treatment Group:
1-2 insulin injections/day, daily glucose
Monitoring, quarterly clinic visits
Intensive Treatment Group:
>3 insulin injections/day or continuous
insulin infusion, frequent glucose
monitoring, frequent contact with health
professionals
The Diabetes Control and Complications Trial Research Group
(1995) The relationship of glycemic exposure (HbA1c) to the risk of
development and progression of retinopathy in the diabetes control
and complications trial, Diabetes, 44:968-983.
11
Positive Symptoms as Biomarkers for Disease
State in Schizophrenia
• Positive symptoms (hallucinations, thought disorders, delusions and
movement disorders) are not the earliest symptoms of schizophrenia:
Cognitive symptoms (attention, working memory and decision-making
deficits) manifest themselves prior to positive symptoms
• However, not everyone with cognitive symptoms progress to positive
symptoms
• Positive symptoms do not track progression, remission and relapse as
well as cognitive symptoms
• Treatments that control positive symptoms do not improve functional
outcome
• Positive symptoms have led to ineffective therapies to treat
schizophrenia: focus now on cognitive symptoms
Day, M, Rutkowski JL, Feuerstein GZ (2009) Translational Medicine-a paradigm shift in modern drug discovery and development:
The role of biomarkers, Pharmaceutical Biotechnology, Eds Carols A. Guzman and Giora Z. Feuerstein, Landes Bioscience and Springer Science+Business Media
12
Biomarkers that Define the ChemicalPhysical Interaction of Drug with a Target
• Parameters include
– Binding of the drug to the target
– Residency time on the target
– Specific site of interaction with the target
– Physical or chemical consequences to the target
induced by the drug
• Steric hindrance, conformational changes, etc
• All of these biomarkers reflect target
engagement by a therapeutic agent
Day, M, Rutkowski JL, Feuerstein GZ (2009) Translational Medicine-a paradigm shift in modern drug discovery and development:
The role of biomarkers, Pharmaceutical Biotechnology, Eds Carols A. Guzman and Giora Z. Feuerstein, Landes Bioscience and Springer Science+Business Media
13
Compound-Target Interaction Biomarker: Binding of the Drug
to the Target
Displacement of [18F]MK-9470 by Taranabant, a Selective CB1 Inverse Agonist in Human Brain
With CNS Side Effects
Horizontal
Section
Coronal
Section
Sagittal
Section
Proximal Biomarker
[18F]MK-9470 positron emission tomography (PET) parametric standardized
uptake value images acquired before (top) and 24 hours after (bottom) the
last dose of taranabant following 14 days of daily treatment with 7.5 mg dose.
Brain CB1R occupancy as assessed by PET imaging with
[18F]MK-9470 as a PET tracer following single or multiple
doses of taranabant. A first-order Hill curve (nH=1)
is fitted through all data.
Addy C, et al., (2008) The acyclic CB1R inverse agonist taranabant mediates weight loss by increasing energy expenditure and decreasing caloric intake
Cell Metabolism, 7:68-78.
14
Pharmacodynamic (PD) Biomarkers:
Biological Consequence of Interaction with the Target
• May include markers of drug-target interaction (target engagement) such
as binding
• Can include therapeutically desired events or adverse events based upon
the target’s mechanism of action
• Could be discrete molecular events at or proximal to the biochemical
pathway modified by manipulation of the target
• May be remote in vitro or in vivo consequences of target manipulation
• May also reflect “off-target” biological effects of the drug not related to
its effects on the target
• Important to understand the biological pathways of the target and
mechanism of action for drug prior to making conclusions about PD
markers
Day, M, Rutkowski JL, Feuerstein GZ (2009) Translational Medicine-a paradigm shift in modern drug discovery and development:
The role of biomarkers, Pharmaceutical Biotechnology, Eds Carols A. Guzman and Giora Z. Feuerstein, Landes Bioscience and Springer Science+Business Media
15
Brain CB1 Receptor Occupancy was Closely
Associated with Efficacy and CNS Adverse Events
Occupancy correlates with plasma concentration and dose; dose correlates with adverse events and efficacy
CB1 Occupancy as a Function
Of Dose and Plasma Concentration
Proximal PD marker
Efficacy (Weight Loss) as a
Function of Dose
Clinical Endpoint
Addy C, et al., (2008) The acyclic CB1R inverse agonist taranabant mediates weight loss by increasing energy expenditure and decreasing caloric intake
Cell Metabolism, 7:68-78.
16
In Search of a CB1 Inverse Agonist Devoid of CNS Side Effects:
Displacement of [11C]MePPEP by TM38837 in Non-Human
Primate Brain
Low Brain Receptor Occupancy
Compared to Rimonabant, a
CNS-Penetrant CB1 Inverse Agonist
TM38837 (1mg/kg or 6 mg/kg) attenuated peripheral,
but not central effects of a CB1 agonist in humans
Remote PD markers
Takano A, et al. (2013) Low brain CB1 receptor occupancy by a
second generation CB1 receptor antagonist TM38837 in comparion
with rimonabnat in nonhuman primates: a PET study, Synapse, 68:89-97.
Klumpers LE, et al. (2013) Peripheral selectivity of the novel cannabinoid receptor
Antagonist TM38837 in healthy subjects, Brit J Clin Pharm, 76(6), 846-857.
17
Pharmacodynamic Biomarkers Indicating Efficacy and Safety
Case Study: Apixaban (Factor Xa Inhibitor)
Proximal Markers of Factor Xa Inhibition:
Ex vivo anti-Factor Xa Activity and
Anti-Thrombin Effects
Correlation of Remote Marker
(Carotid Blood Flow)
With Proximal Marker
(Ex vivo Factor Xa Inhibition)
Correlation of drug plasma
concentration with proximal
marker (pK/PD)
Along with efficacy markers of thrombus
formation, the safety marker (bleeding time)
can be used as a therapeutic index
Wong PC, Crain EJ, Xin B, Wexler RR, Lam PYS, Pinto DJ, Luettgen JM, Knabb RM (2008) Apixaban, an oral, direct and highly selective factor Xa inhibitor: in vitor, antithrombotic and antihemostatic studies
J Thrombosis and Haemostasis, 6: 820-829.
18
“Remote” Pharmacodynamic Markers for Taranabant Allow
an Initial Evaluation of Efficacy at Reduced Cost, but do not
Allow Accurate Dose Estimation
PD1: 24-Hour Food Intake after
Administration of single doses
Of placebo, taranabant or sibutramine
PD2: Changes in Energy Expenditure after
administration of single doses of
taranabant or sibutramine
PD1 better predictor of clinical endpoint than
PD2, BUT neither one is sensitive enough
for dose estimation
Actual Clinical Endpoint:
Weight Loss
Addy C, et al., (2008) The acyclic CB1R inverse agonist taranabant mediates weight loss by increasing energy expenditure and decreasing caloric intake
Cell Metabolism, 7:68-78.
19
“Remote” Pharmacodynamic Marker Predicted Lack of Efficacy:
MK-0493, a Melanocortin-4 Receptor Agonist
PD Marker=24 Hour Food Intake
Geometric mean for various test meals and 24 hr total
Following a single administration of MK-0493 or sibutramine
Clinical Efficacy Endpoint=
12 week Change in Body Weight
Change in body weight by treatment after 12 weeks of dosing
LS=Least squares means (Last observation carried forward, 84% CI)
RMA=repeated measures analysis
Krishna R, et al. (2009) Potent and selective agonism of the melanocortin receptor 4 with MK-0493 does not induce weight loss in obese human subjects:
Energy intake predicts lack of weight loss efficacy. Clin Pharmacol Therap, 86: 659-666.
20
Functional MRI as a Remote Pharmacodynamic
Measure
•
What is functional MRI (fMRI)?
• The application of magnetic resonance to image
physiological changes rather than structural changes
• Blood-oxygen-level-dependent (BOLD) contrast is the
most widely utilized type of fMRI
• Based on the principle that increased neuronal activity is
associated with a local hemodynamic response (increased
cerebral blood flow and blood volume)
• Measured as the ratio of deoxyhemoglobin:hemoglobin
• Deoxyhemoglobin is paramagnetic and reduces MRI signal
• Increased blood flow=reduced oxygen extraction=
reduced deoxyhemoglobin:hemoglobin=increased MRI signal
•
Utilization of fMRI as a remote pharmacodynamic measurement
• Often use a “probe” stimulation to induce a signal
• Measure physiological changes in neuronal activity induced by
drug administration
• Correlate brain activity with behavioral effects of drug administration
• Characterize the way the activity induced by a probe task is modulated by a drug
• Functionally and anatomically specific pharmacokinetic parameters
• Could be used as a clinical surrogate to behavioral or physiological
measures that may be insensitive to drug effects early in treatment
•
Caveats
• Long and short term pharmacological effects could be different
• If fMRI effect requires a “probe” stimulation, the choice of probe could
determine sensitivity and nature of response to drug
• Anything that alters blood flow by effects on the vasculature could
confound the interpretation of the drug effect
• Could relate fMRI result to EEG, evoked potential, receptor
occupancy or a measure which is not confounded by changes in blood flow
21
Remote Pharmacodynamic Markers for
Schizophrenia Therapeutics
Translating from
rodents to humans:
Ketamine infusion
increases overall
global-based
connectivity in both
species and is
associated with
schizophrenia-like
symptoms
( (Positive and Negative
Symptoms Scale
(PANSS))
Biomarkers Med. (2014) 8(1), 29-49
22
Biomarkers that Validate the Importance of a Target
in Disease and Drug Development
• Target/Biomarker: Estrogen Receptor Alpha (expression)
– Disease: breast cancer
– Drug: Tamoxifen (ER alpha antagonist)
• Target/Biomarker: Human Epidermal Growth Factor Receptor 2
(HER2/Neu) (overexpression or amplification)
– Disease: breast cancer
– Drug: Herceptin (trastuzumab: monoclonal antibody to HER2
• Target/Biomarker: Epidermal Growth Factor Receptor (expression or
activating mutation)
– Diseases:
• Gefitinib: non-small cell lung cancer (NSCLC), breast cancer
• Cetuximab: NSCLS, colo-rectal cancer, head and neck cancer
• Panitumab: Metastatic colo-rectal cancer
– Drugs: Gefitinib (Tyrosine Kinase domain), Cetuximab, Panitumab: all
monoclonal antibodies to EGFR
Weigel MT, Dowsett M (2010) Current and emerging biomarkers in breast cancer: prognosis and prediction, Endocrine-Related Cancer, 17:R245-R262.
Chau CH, Rixe O, McLeod H, Figg WD (2008) Validation of analytical methods for biomarkers employed in drug development, Clin Cancer Res, 14(19):5967-5976.
23
Biomarkers Can Inform Patient
Selection and Prognosis
• Particularly helpful in proof of concept or confirmation
Phase III trials required for drug registration
• Could answer questions such as
– Who will respond to the drug
– Who may have drug-related adverse effects
– Patient prognosis
– Tumor recurrence
• Examples of patient selection biomarkers
– Genetic (SNP, haplotypes, gene expression)
– fMRI in response to stimuli known to activate drug-related
pathway
Day, M, Rutkowski JL, Feuerstein GZ (2009) Translational Medicine-a paradigm shift in modern drug discovery and development:
The role of biomarkers, Pharmaceutical Biotechnology, Eds Carols A. Guzman and Giora Z. Feuerstein, Landes Bioscience and Springer Science+Business Media
24
Utility of Biomarkers in the Clinical Evolution of Cancer
Choose the Right Patient and the Right Treatment
25
Ludwig JA, Weinstein JN (2005) Biomarkers in cancer staging, prognosis and treatment selection, Nature Reviews Cancer, 5:845-856.
HER2 and HER2:HER2 Dimer Expression as
Predictors of Clinical Benefit in Breast Cancer
Correlation of HER2 Expression with Clinical Benefit from Trastuzumab (Herceptin)
Modest correlation
Overexpression of HER2 initially indicator
of poor prognosis for breast cancer
Truncated HER2 receptor indicates poor
response to Herceptin, but benefit from
Lapatinib (tyrosine kinase inhibitor)
CR=Complete Response
PR=Partial Response
SD=Stable Disease
Desmedt C, et al. (2009) Quantitation of HER2 expression
or HER2:HER2 dimers and differential survival in a cohort of
metastatic breast cancer patients carefully selected for
26
Trastuzumab treatment primarily by FISH, Diagn Mol Pathol
18:22-29.
Estrogen Receptor and Progesterone Receptor Expression Can
Predict Time to Recurrence in Patients Receiving Tamoxifen and
Anastrozole
Quartile of Estrogen Receptor H-score
Tamoxifen=Estrogen Receptor Antagonist
Anastrozole=Aromatase Inhibitor
Quartile of Progesterone
Receptor staining
Weigel M, Dowsett M (2010) Current and
Emerging biomarkers in breast cancer:
Prognosis and prediction, Endocrine-Related
Cancer 17:R245-R262.
27
Drug Metabolism Biomarkers Can Also Predict
Drug Responses: Some Examples
• CYP2C9
– Poor Metabolizer (PM) variants increase and extensive
metabolizer (EM) variants decrease Celecoxib (Celebrex) drug
exposure and side effect risk
– Mutations increase bleeding risk of Warfarin, suggesting
reduced dosage
• CYP2C19
– Variants with genetic defect lead to change in Voriconazole
(anti-fungal agent) exposure (PM variant increases drug
exposure and toxicity)
• CYP2D6
– PM variants increase and EM variants decrease Fluoxetine HCl
(Prozac; antidepressant) exposure and toxicity
*CYP=cytochrome P450
28
Chau CH, Rixe O, McLeod H, Figg WD (2008) Validation of analytical methods for biomarkers employed in drug development, Clin Cancer Res, 14(19):5967-5976.
Case Study: Should CYP2C19 Be Used as a Biomarker to Predict the
Response to the Anti-Thrombotic Agent, Clopidogrel, and
Subsequent Ischemic Events?
*Increased incidence of ischemic
events due to reduced absorption
of clopidogrel
Benefit: Risk (Cost) Assessment of
Approving a Pharmacogenomic Marker for
Response to Clopidogrel
Decision: No reimbursement for use of CYP2C19 as a biomarker
for Clopidogrel response
Increase in Ischemic Events Due to Reduced Response
to Clopidogrel is Modest and Similar to Risk of Bleeding
Induced by Response to Clopidogrel
Pare G, Eikelboom JW (2011) CYP2C19 genetic testing should not be done in all patients treated clopidogrel who are undergoing percutaneous coronary intervention,
Circulation Cardiovascular Interventions, 4:514-521.
29
How Are Biomarkers Developed? Process in
Oncology as an Example
FDA Approval Not Needed
High Hurdle
Center for Medicaid and Medicare
Services (CMS): Determine Whether
The Test is Reimburseable
Ludwig JA, Weinstein JN (2005) Biomarkers in cancer staging, prognosis and treatment selection, Nature Reviews Cancer, 5:845-856
30
Biomarker Discovery and Development: Choice of
Assays
•
Choice depends on the biomarker target, application of the biomarker and limitations of the respective
technology
•
Genomics Approach
–
•
Proteomics Approach
–
•
Profiling endogenous metabolites in biofluids or tissue using NMR, LC/MS; principally used in biomarker discovery
Imaging
–
–
–
•
Global protein profiling (abundance, location, modification and protein-protein interactions); principally used in biomarker
discovery. Immunoassays primarily used for biomarker validation
Metabolomics
–
•
Microarrays (Biomarker discovery), reverse transcription-polymerase chain reaction (rtPCR), comparative genomic
hybridization
Molecular and functional to assess cell proliferation/apoptosis (18F-fluoro-L-thymidine), cellular metabolism (18Ffluorodeoxyglucose PET)
Dynamic contrast-enhanced CT and MRI to assess angiogenesis/vascular dynamics, beta-cell mass, liver fibrosis
fMRI with environmental stimuli/interaction to assess patient-specific brain function from motivational state to cognitive
function
Functional
–
–
–
–
EEG (basal and stimulated)
Food intake
Resting metabolic rate and respiratory quotient
Attention tests (sensory gating, etc)
31
Chau, C.H., et al., Validation of Analytical Methods for Biomarkers Employed in Drug Development, Clin Cancer Res, 2008; 14(19): 5967-5976.
Biomarker Assay Validation Process:
Considerations
•
Can be more complex than typical bioanalytical assay that follows Good Laboratory Practice (GLP) guidelines
•
Biological matrix (contains the biomarker)
–
–
–
•
Must be meaningful relative to biomarker physiology, readily accessible from patient, while taking into account assay sensitivity
Procedures for sample collection, handling, storage must be standardized
Handling and storage of reagents should also be standardized, with integrity of reagents optimized
Quality control measures (Document analytical performance, determine acceptance or rejection of an analytical
run during sample analysis)
–
–
–
Requires a systematic review of analyte stability in calibration standards, quality control (QC)samples and experimental samples
QC samples used to judge acceptability of assay runs (evaluate lower, middle and upper regions of standard curve)
VS (validation samples) used to evaluate intra and inter-run accuracy, precision and stability
•
–
Calibration curve
•
•
•
•
•
Should use at least 5 different concentrations of VS, in duplicate on at least 6 different runs during pre-study validation since
biomarker assays often show nonlinear calibration curves
May be difficult to find an analyte-free matrix for calibration or specificity studies
Target biomarker often not available to use as a certified standard, so must rely on non-certified standard for construction of
calibration curve
If assay standards are prepared in a non-authentic matrix, QC samples should be prepared and tested in the same matrix as the study
samples to show similarity between the authentic and non-authentic matrices
Dilution linearity can also be an issue, since antibody and ligand binding can vary in different matrices
Other considerations
–
–
–
–
–
–
Reference materials
Dynamic range
Precision and Accuracy
Sample recovery
Sample volumes
Instrument validation
Chau, C.H., et al., Validation of Analytical Methods for Biomarkers Employed in Drug Development, Clin Cancer Res, 2008; 14(19): 5967-5976.
32
Analytical Aspects of Assay Validation
•
Sensitivity
–
Ability of biomarker to be measured with adequate precision
–
Limit of quantitation (LOQ):lowest concentration at which an analyte can be reliably distinguished from zero
•
Dynamic range: “window” of reliable detection
•
Specificity
–
–
•
•
“Hook” effect in “sandwich” (two-site) immunoassays: high concentrations of the analyte saturate all antigen binding sites on the capture and label reagent
antibodies, interfering with the sandwich formation: results in a signal that is much lower than the expected value
•
Anti-reagent antibodies found in patient samples (human anti-mouse antibodies, rheumatoid factor)
•
Nonspecific interferences
Carryover: materials are unintentionally transferred from one reaction mixture into another, leading to inaccurate concentration data
•
Can occur when a low concentration sample is analyzed immediately after a high concentration sample
•
Documented for cases of tumor markers such as AFP (alpha feto protein), hCG (human chorionic gonadotrophin) and carcinoembryonic antigen (CEA)
•
Mitigate by using disposable pipette tips and samples cups, optimize probe design and system wash procedures
Precision
–
–
–
•
Degree to which unrelated matrix components cause analytical interference (altering the correct value of the result)
Repeatability
Reproducibility
Robustness
Accuracy: degree of agreement between the average value of test results and an accepted reference value
–
–
–
–
Quantitatively expressed as bias (discrepancy between average measured value and reference value)
Constant bias: results differ from reference by a fixed amount
Proportional bias: results differ from reference as a function of the analyte concentration
Bias can be an issue when multiple assays are available for a biomarker; bias in one assay will complicate diagnosis and monitoring
•
•
•
–
Example: immunoassays are influenced by specificites of antibodies used in assay
Tumor markers can exist as a family of isoforms and antibodies can recognize distinct subsets of the family
Need to have clear understanding of what forms are being measured; important part of evaluation process
Assay calibration can also produce bias; uses reference agent to calibrate the instrument signal; reference agent composition should closely resemble
patient sample
Fuzery AK, et al. Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges, Clinical Proteomics, 2013, 10: 1-14.
33
Dynamic Range
The test method should perform acceptably through the range of its intended use.
At minimum, should increase the range of the test by the amount of uncertainty in
the test method. The bell curves in both illustrations show the degree of variablity
in the assay; this defines the added range designed for the test.
Orzechowski AP , Understanding Test Method Development and Validation, A Comprehensive Guide to the Strategy and Scientific Approach for Test Method Development
A Best Practices Technical Reference Manual Abbott Laboratories
34
Analytical Aspects of Assay Validation
•
Sensitivity
–
Ability of biomarker to be measured with adequate precision
–
Limit of quantitation (LOQ):lowest concentration at which an analyte can be reliably distinguished from zero
•
Dynamic range: “window” of reliable detection
•
Specificity
–
–
•
•
“Hook” effect in “sandwich” (two-site) immunoassays: high concentrations of the analyte saturate all antigen binding sites on the capture and label reagent
antibodies, interfering with the sandwich formation: results in a signal that is much lower than the expected value
•
Anti-reagent antibodies found in patient samples (human anti-mouse antibodies, rheumatoid factor)
•
Nonspecific interferences
Carryover: materials are unintentionally transferred from one reaction mixture into another, leading to inaccurate concentration data
•
Can occur when a low concentration sample is analyzed immediately after a high concentration sample
•
Documented for cases of tumor markers such as AFP (alpha feto protein), hCG (human chorionic gonadotrophin) and carcinoembryonic antigen (CEA)
•
Mitigate by using disposable pipette tips and samples cups, optimize probe design and system wash procedures
Precision
–
–
–
•
Degree to which unrelated matrix components cause analytical interference (altering the correct value of the result)
Repeatability
Reproducibility
Robustness
Accuracy: degree of agreement between the average value of test results and an accepted reference value
–
–
–
–
Quantitatively expressed as bias (discrepancy between average measured value and reference value)
Constant bias: results differ from reference by a fixed amount
Proportional bias: results differ from reference as a function of the analyte concentration
Bias can be an issue when multiple assays are available for a biomarker; bias in one assay will complicate diagnosis and monitoring
•
•
•
–
Example: immunoassays are influenced by specificites of antibodies used in assay
Tumor markers can exist as a family of isoforms and antibodies can recognize distinct subsets of the family
Need to have clear understanding of what forms are being measured; important part of evaluation process
Assay calibration can also produce bias; uses reference agent to calibrate the instrument signal; reference agent composition should closely resemble
patient sample
Fuzery AK, et al. Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges, Clinical Proteomics, 2013, 10: 1-14.
35
Precision = Repeatability, Reproducibility and
Robustness
Figure 2-6 shows repeatability
if data is collected within-runs and
shows reproducibility if data
is collected between runs.
Figure 2-8 shows robustness: data is
collected on different instruments, with
different analysts, on different days, etc.
One method of calculating precision
Orzechowski AP , Understanding Test Method Development and Validation, A Comprehensive Guide to the Strategy and Scientific
Approach for Test Method Development: A Best Practices Technical Reference Manual Abbott Laboratories
36
Analytical Aspects of Assay Validation
•
Sensitivity
–
Ability of biomarker to be measured with adequate precision
–
Limit of quantitation (LOQ):lowest concentration at which an analyte can be reliably distinguished from zero
•
Dynamic range: “window” of reliable detection
•
Specificity
–
–
•
•
“Hook” effect in “sandwich” (two-site) immunoassays: high concentrations of the analyte saturate all antigen binding sites on the capture and label reagent
antibodies, interfering with the sandwich formation: results in a signal that is much lower than the expected value
•
Anti-reagent antibodies found in patient samples (human anti-mouse antibodies, rheumatoid factor)
•
Nonspecific interferences
Carryover: materials are unintentionally transferred from one reaction mixture into another, leading to inaccurate concentration data
•
Can occur when a low concentration sample is analyzed immediately after a high concentration sample
•
Documented for cases of tumor markers such as AFP (alpha feto protein), hCG (human chorionic gonadotrophin) and carcinoembryonic antigen (CEA)
•
Mitigate by using disposable pipette tips and samples cups, optimize probe design and system wash procedures
Precision
–
–
–
•
Degree to which unrelated matrix components cause analytical interference (altering the correct value of the result)
Repeatability
Reproducibility
Robustness
Accuracy: degree of agreement between the average value of test results and an accepted reference value
–
–
–
–
Quantitatively expressed as bias (discrepancy between average measured value and reference value)
Constant bias: results differ from reference by a fixed amount
Proportional bias: results differ from reference as a function of the analyte concentration
Bias can be an issue when multiple assays are available for a biomarker; bias in one assay will complicate diagnosis and monitoring
•
•
•
–
Example: immunoassays are influenced by specificites of antibodies used in assay
Tumor markers can exist as a family of isoforms and antibodies can recognize distinct subsets of the family
Need to have clear understanding of what forms are being measured; important part of evaluation process
Assay calibration can also produce bias; uses reference agent to calibrate the instrument signal; reference agent composition should closely resemble
patient sample
Fuzery AK, et al. Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges, Clinical Proteomics, 2013, 10: 1-14.
37
Accuracy: Difference from Precision
Actual Value=“bull’s eye”
Dots are sample values
Refers to Conditions 1-4
In Figure 2-12
Accuracy=Closest agreement between a test result and a known or given value
Precision=Variability as a function of repeatability, reproduceability and robustness
Accuracy can be determined based on a reference standard, recovery and dilution
Recovery:Accurate quantities of analyte added to sample and incremental increase in measured concentration is determined
recovery=measured increase in concentration/predicted increase in concentration x 100%
Dilution: Do dilutions of the sample follow the calibration curve? Calculate least squares regression on corrected sample values
vs value of original sample and examine 95% confidence interval for slope of line
Orzechowski AP , Understanding Test Method Development and Validation, A Comprehensive Guide to the Strategy and Scientific Approach for Test Method Development
A Best Practices Technical Reference Manual Abbott Laboratories
38
Clinical Aspects of Assay Validation
(Biomarker Qualification)
•
Robust analytical validation essential, but biomarker assays still fail in clinical practice:
clinical validation extremely important
•
Clinical validation process
– Define intended clinical use up front: Clinical performance of a biomarker will vary across
clinical context, disease spectra and patient subgroups
– Determine clinical sensitivity (correctly identify patients with the disease) and specificity
(correctly identify those without the disease)
• Exploratory phase: can the test discriminate between overt disease and normal controls?
• Challenge phase: can the test discriminate between patients with subtle disease or confusing
aspects of the disease and normal controls?
• Advanced phase: can the test discriminate between those with and without disease in patients
representative of the target population?
• Determine overall assay performance using Receiver Operating Characteristic (ROC) analysis:
graph clinical sensitivity vs 1- clinical specificity, use area under the curve
• Assess positive predictive value (PPV) and negative predictive value (NPV): combine test’s clinical
sensitivity and specificity with disease prevalence: disease with low prevalence can have good
diagnostic accuracy, but low PPV (i.e., CA-125 for ovarian cancer)
• Recommendation from Interdisciplinary Pharmacogenomics Review Group (IPRG) Biomarker
Qualification Review Team for Drug Safety Biomarkers
•
Minimize cost and assay turn-around time
Fuzery AK, Levin J, Chan MM, Chan DW (2013) Translation of proteomic biomarkers in FDA approved cancer diagnostics: issues and challenges, Clinical Proteomics, 10:13
39
FDA Approval Process for Clinical Biomarkers
•
•
Diagnostic biomarkers are considered medical devices
– Regulated by Office of In Vitro Diagnostics and Radiological Health (FDA)
– Same regulatory standards as other types of medical devices
Three Classes (depending on intended use and risk to the patient if the device provides incorrect results)
– Class III: Incorrect results pose significant risk to patient
•
–
Class II: Incorrect results pose moderate risk to patient
•
•
•
–
Reviewed by FDA through premarket notification (510k) pathway
FDA “clears” the device for marketing if no concerns are raised by the FDA within 90 days after submission of the 510(k)by the sponsor
Sponsor must show that the new device is substantially equivalent to a predicate device (which originally showed that it was safe and
effective); if clinical studies required, a premarket inspection is not required, which speeds up the time to market (de novo submission)
Class I: Incorrect results pose lowest risk to patient
•
•
•
Require premarket approval by FDA: sponsor must demonstrate that the device is safe and effective for patient care; must submit a
premarket approval application (PMA), conduct clinical studies to prove safety/efficacy and submit to a premarket inspection
Does not require premarket submission to FDA
Sponsor must register and list the device with the FDA and is responsible for quality control aspects of marketing the device
FDA approval process
– Presubmission: Sponsor prepares statement of assay’s intended use, description of the technology and proposal for
analytical and clinical studies. FDA and sponsor agree on whether the sponsor must submit a PMA, 510(k) or de novo
submission
•
–
–
–
Investigational Device Exemption (IDE) required if clinical studies pose signficant risk to patient (data used for patient care or if the study
involved invasive tissue sampling)
Sponsor submits a 510(k) or PMA application; agency must review within 90 days for a 510(k) or 180 days for a PMA
If Class II device cleared, can be legally marketed. Requirements vary depending upon which type of CLIA laboratory the
test will be conducted in or whether the test is for home use.
For Class III device, FDA determines if device is safe and effective and will require a premarket inspection of the sponsor
•
Post marketing responsibilities include: reporting any changes in manufacturing or design, where anything that could impact safety or
efficacy must be reviewed by FDA
Fuzery AK, Levin J, Chan MM, Chan DW (2013) Translation of proteomic biomarkers in FDA approved cancer diagnostics: issues and challenges, Clinical Proteomics, 10:13
40
Several Levels of Biomarker Validation and
Qualification: Which Process Should Be Used?
Measured in analytical test system with
well-established performance characteristics
and have sound scientific basis with predictive
value of clinical outcome
Scientific community accepts
these biomarkers as predictors
of clinical outcome
Fill gaps of uncertainty
in Discovery phase;
i.e., gene panels for
preclinical safety evaluation
41
Chau, C.H., et al., Validation of Analytical Methods for Biomarkers Employed in Drug Development, Clin Cancer Res, 2008; 14(19): 5967-5976.
Biomarkers and the Future of
Drug Development
Precision Medicine
42
Precision Medicine
Correlating Biomarker/Molecular Information with Specific
Clinical Phenotype
Goal: Establish biomarker/molecular information as predictive of clinical phenotype
•
Pharmacotherapeutic Potential
–
–
•
Therapeutic and diagnostic examples
–
–
–
•
Improve therapeutic index (increase probability of efficacy while decreasing probability of adverse
events)
Biomarker-directed drug and dose selection
Increased from 13 examples in 2006 to 113 examples in 2014 (primarily oncology, but moving outside
this area)
Current examples are from companion diagnostics, where the biomarker represents the molecular
target of the drug
Future will likely include biomarker panels and large scale sequencing approaches for more diseases
with multiple biomarker interactions with a clinical phenotype
Advantages of companion diagnostics
–
–
–
–
–
Developed and co-marketed with therapeutic agent
Increased likelihood that treatment will be safe and effective
Can target drugs to patients that will respond; clearer path to optimized benefit/risk profile
Accelerate drug development by enabling enrichment of responder populations in clinical trials
Examples: vemurafenib and dabrafenib for BRAF V600 mutation in melanoma, ivacaftor for G551D
mutation in cystic fibrosis, aftinib and erlotinib for EGFR mutations in lung cancer
Vicini, P, et al., doi: 10.1002/cpt.293
43
FDA-Approved Protein Biomarkers Currently Used in Clinical
Oncology Practice
44
Fuzery AK, Levin J, Chan MM, Chan DW (2013) Translation of proteomic biomarkers in FDA approved cancer diagnostics: issues and challenges, Clinical Proteomics, 10:13
Beyond Single-Gene Companion Diagnostics: Preemptive
Pharmacogenomic Testing and Approach to Alzheimers and
Parkinsons Disease
Preemptive Pharmacogenomic Testing
• Example: Prospective genotyping for
common polymorphisms within 34
genes associated with drug metabolism,
absorption, distribution, and excretion
Alzheimers and Parkinsons Disease
• Complex pathophysiology combined
with progressive phenotypic expression
• Approach
–
–
–
Precision Medicine: 3 Key Elements
Comprehensive risk assessment (gene x
environment)
Detection of latent pathophysiological
processes (imaging, Lewy body formation,
etc)
Molecularly tailored interventions (Suppress
or reverse latent pathophysiological processes
in subsets of patients with shared molecular
markers)
Vicini, P, et al., doi: 10.1002/cpt.293 and Montine, TJ, et al., J Experimental Med, 2015; 2 (5), 601-605
45
Biomarkers and the Future of Drug Development:
An Integral Part of Drug Discovery From Target Validation
Through Clinical trials
46
Borsook D, Becerra L, Hargreaves R (2011) Biomarkers for Chronic Pain and Analgesia. Part 1: The Need, Reality, Challenges and Solutions, Discovery Medicine, Vol 11 (57): 197-207.
The Drug Discovery and Development Process:
A Quick Review
Potency/Selectivity
PK/ADMET
SAR pharmacophore
POC animal efficacy
Dose Range Finding (DRF) studies in 2 species
PK studies to support DRF
Chronic toxicology studies in 2 species
Safety pharmacology studies
Process and formulation research
GMP manufacturing
Is This Process Still Working?
Blank, S. “Reinventing Life Science Start-Ups-Evidence Based Entrepreneurship”, 2013
47
Review Questions
•
What constitutes a translational approach to drug discovery and development
and why is it important in current models of pharmaceutical research and
development?
•
What is the role of biomarkers in drug discovery and development and how do
they contribute to translational research and medicine?
•
What are pharmacodynamic markers and what role do they play in
translational medicine?
•
How can biomarkers inform patient selection and prognosis?
•
What steps are involved in the biomarker validation and qualification process?
When is FDA approval necessary?
•
•
•
•
What are the components of assay validation from an analytical perspective?
What are the components of assay validation from a clinical perspective?
Describe the FDA approval process for Class II and III biomarkers
How will biomarker development be involved in drug development of the
future?
48
Productivity Has Leveled Off Since the 1990’s Without A
Significant Increase in New Drugs
Productivity=
Peak Sales/Spending
Below cost of capital
NTD=New Therapeutic Drugs
Peak Sales Have Generally
Followed Number of NTDs
Schulze, U., Baedeker, M, Chen, Y-T, Greber, D (2014), R&D productivity:
on the comeback trail, Nature Reviews Drug Discovery, Advance Online Publication, 1-2
49
Why Are There Fewer New Drugs?
2011-2012
Metabolic Disease,
Endocrinology
50
Arrowsmith J, Miller P, (2013) Phase II and Phase III attrition rates, 2011-2012, Nature Reviews Drug Discovery; 12:569
The Traditional Drug Discovery Process:
Room for Improvement
• Pharmaceutical industry’s price/earnings ratio has decreased
below that of the S&P500 index and has remained flat for the
past 7 years
• Generic drugs are approaching 70% of all prescriptions
written in the U.S.
• Key patent expirations between 2010-2014 have put more
than $209 billion in annual drug sales at risk, resulting in $113
billion of sales lost to generic substitution
• Dramatic reduction in the number of innovative new
medicines approved by the FDA/EMA
– In the past 9 years, 50% fewer new drugs (new molecular
entities or NMEs) were approved compared with the previous
decade
Paul, SM, Mytelka, D.S., Dunwiddie, CT, Persinger, CC, Munos, BH, Lindborg, SR, Schact, AL., (2010),
How to improve R&D productivity: the pharmaceutical industry’s grand challenge, Nature Reviews, Drug Disovery; 9:203-214.
51

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