Molecular Therapy
Pharmacodynamics and -kinetics
Lecture: Personalized healthcare
Principle of personalized healthcare
- The right therapy for the right patients at right intensity at right time
- Molecular biomarkes as key drivers of patients selection
- = personalized medicine, precision medicine
- Personalized healthcare: Drug not toxic and beneficial
Example: melanoma
BRAF mutation
- Protein: part of the ERK pathway (growth pathway)
o ERK pathway: all kinases, 2 phosphorylation sites who both need to be
phosphorylated to initiate kinase cascade
If there is a mutation, these cell will grow uncontrolled, and become cancer cells
- Drug development: inhibition of this pathway = kinase inhibitor, thus B-RAF inhibitor (nib at
end of drug means kinase inhibitor)
60% of melanoma patients have a BRAF mutation, so this is a good basis for personalized medicine!
- ZELBORAF (vemurafenib) is a successful B-RAF inhibitor used in clinical practice for
melanoma
o However; problem occurred → drug resistance
o Combination: drug + chemo
Biomarkers:
- Characteristic that is objectively measured and evaluated as an indicator of normal
biological processes, pathogenic processes, or pharmacologic responses to a therapeutic
intervention
o molecular biomarkers provide a molecular impression of a biological system
o biomarkers can be carious sorts of data, of combinations thereof
biomarker data can be used to make driver decisions during drug discovery and development
- 5R’s assessment
o Right target
o Right tissue
o Right safety
o Right patients
o Right commercial potential
Leading to an increase of R&D productivity
Drug discovery and development phases
1. Research
2. Lead finding (high throughput screening, is a drug good for clinical)
3. Preclinical (toxicity and bioavailability)
4. Phase 1 (low dose)
5. Phase 2 (right dose)
, 6. Phase 3 (more people, 1 dose)
Personalized healthcare
- In rare metabolic diseases
Story: normal Dutch parents, 2 children (boys), both died (age 3.5, 1.5)
Symptoms: low birth weight, lactic acidosis, hypoglycaemia, intellectual disability, movement
disorder, epilepsy
What did they see after lab tests:
1. Biomarkers for mitochondrial disease where low: ATP production and creatine phosphate
production
2. But no diversions in the OXPHOS enzyme complexes
3. No variant found in candidate sequencing
But, after whole exome sequencing: mutation on WARS2 was found
- WARS2 is mtDN-coded tryptophanyl-tRNA synstase
- Mutation caused instability of the WARS2 protein leading to less charging of Trp-tRNA =
mitochondrial encephalopathy
A new prenatal genetic test was developed to screen for this mutation in unborn children
- Disease was maternal inherited and X-linked
= example of personalized healthcare
Lessons learned:
1. Technology innovation is driving impact I personalized healthcare
2. Crucial to combine different molecular views to understand human health and disease (X-
omics)
3. Fast translation of biomarker research to implementation needed!
Omics: looking at everything
Role of omics in personalized healthcare
- Leads to
- Personalized diagnosis
- Personalized therapy
- Patient-doctor interaction
Power of omics in molecular analysis
Genomics: 3.000.000.000 DNA basis in 1 assay
Proteomics, glycomics, metabolomics: 10.000-50.000 proteins and metabolites in 1 assay
- Higher diagnostic yield
- Contextualisation of change (giving the bigger picture)
Integration of clinical omics data will drive personalized healthcare
1. Personalised diagnosis: by – omics
2. Elucidating new disease mechanisms: by deep learning, artificial intelligence, system biology
3. And monitoring new personalized (combination) therapies
Translational medicine: laboratory to society
- Using digital biomarkers
Laboratory: up to 1.000.00 signals per analysis
Society: point-of-care analysis of few biomarkers
,Lot of data: but not all date is useful data!
- Quality, not quantity
Translational innovation gaps
1. Research to research
2. Research to diagnostics
3. Research to society
So a lot of data: but half of the published data
cannot be reproduced
- Due to biological reagent and reference
materials
- Study design
- Data analysis and reporting
- Laboratory protocols
Example: biomarkers to support drug development in melanoma
- Need for blood-based biomarkers that indicate downstream effects of drug
o Inhibition of ERK pathway (pharmacodynamic)
o Tumor inhibition (efficacy)
After extensive transcriptomics profiling: IL-8 was a promising candidate biomarker
But, they also did a validation study to confirm IL-8 in melanoma to
1. Confirm elevated IL-8 in melanoma
2. Develop IL-8 assays for clinical use
- But IL-8 protein levels did not increase (significant) in plasma and serum (even though
literature confirmed this should be the case)
This was not published, because only positive outcomes are shared
- If we want to innovate clinical molecular biomarkers, we need to increase quality, not
quantity of our research
So in personalized healthcare
- Always focus on the end user: patient/ citizen
3 key aspects of personalized health(care)
1. What to measure
2. How much can it change
3. What should be the follow up for me
So, translation is key
Personal data → knowledge → understanding → decision → action
- Need for personalized data-driven GPS for health
o Monitor on background
o Alert when at risk
o Advice what to do
o Doctor as coach
And know, all this data can give a ‘funhouse mirror effect’: there is no single one reflection of health
, Tutorial: Pharmacology and drug disposition
Pharmacology: the science that is concerned with the use, effects and modes of action of chemicals
on the function of living systems
- Multidisciplinary field: molecule, cell, organ, organism, family and population
Pharmacotherapy:
- Patient
o What does the patient with the drug
o What does the drug with the patient
1. Pharmacokinetics: how does the drug travel through the body
2. Pharmacodynamics: if the drug reaches the target, how does it act?
You have both rational pharmacotherapy and evidence-based pharmacotherapy
1. Rational: mechanism-based pharmacotherapy
2. Evidence-based: does not matter how it works, as long as it works
Pharmacological phases in
pharmacotherapy
1. Exposition phase
2. Toxicokinetic phase
3. Toxicodynamic phase
Classification of pharmacological effects:
Agonist: activation of e.g. receptor → intracellular signal transduction
Antagonist: blocking of receptor → dysfunctional or no function of receptor
Classical drug-receptor interactions
1. Ligand-gated ion channels: milliseconds
2. G-protein-coupled receptors: seconds
3. Kinase-linked receptors: hours
4. Nuclear receptors: hours
- Thus, main difference = time-scale
Drug binding sites and interaction types
Pharmacodynamics and -kinetics
Lecture: Personalized healthcare
Principle of personalized healthcare
- The right therapy for the right patients at right intensity at right time
- Molecular biomarkes as key drivers of patients selection
- = personalized medicine, precision medicine
- Personalized healthcare: Drug not toxic and beneficial
Example: melanoma
BRAF mutation
- Protein: part of the ERK pathway (growth pathway)
o ERK pathway: all kinases, 2 phosphorylation sites who both need to be
phosphorylated to initiate kinase cascade
If there is a mutation, these cell will grow uncontrolled, and become cancer cells
- Drug development: inhibition of this pathway = kinase inhibitor, thus B-RAF inhibitor (nib at
end of drug means kinase inhibitor)
60% of melanoma patients have a BRAF mutation, so this is a good basis for personalized medicine!
- ZELBORAF (vemurafenib) is a successful B-RAF inhibitor used in clinical practice for
melanoma
o However; problem occurred → drug resistance
o Combination: drug + chemo
Biomarkers:
- Characteristic that is objectively measured and evaluated as an indicator of normal
biological processes, pathogenic processes, or pharmacologic responses to a therapeutic
intervention
o molecular biomarkers provide a molecular impression of a biological system
o biomarkers can be carious sorts of data, of combinations thereof
biomarker data can be used to make driver decisions during drug discovery and development
- 5R’s assessment
o Right target
o Right tissue
o Right safety
o Right patients
o Right commercial potential
Leading to an increase of R&D productivity
Drug discovery and development phases
1. Research
2. Lead finding (high throughput screening, is a drug good for clinical)
3. Preclinical (toxicity and bioavailability)
4. Phase 1 (low dose)
5. Phase 2 (right dose)
, 6. Phase 3 (more people, 1 dose)
Personalized healthcare
- In rare metabolic diseases
Story: normal Dutch parents, 2 children (boys), both died (age 3.5, 1.5)
Symptoms: low birth weight, lactic acidosis, hypoglycaemia, intellectual disability, movement
disorder, epilepsy
What did they see after lab tests:
1. Biomarkers for mitochondrial disease where low: ATP production and creatine phosphate
production
2. But no diversions in the OXPHOS enzyme complexes
3. No variant found in candidate sequencing
But, after whole exome sequencing: mutation on WARS2 was found
- WARS2 is mtDN-coded tryptophanyl-tRNA synstase
- Mutation caused instability of the WARS2 protein leading to less charging of Trp-tRNA =
mitochondrial encephalopathy
A new prenatal genetic test was developed to screen for this mutation in unborn children
- Disease was maternal inherited and X-linked
= example of personalized healthcare
Lessons learned:
1. Technology innovation is driving impact I personalized healthcare
2. Crucial to combine different molecular views to understand human health and disease (X-
omics)
3. Fast translation of biomarker research to implementation needed!
Omics: looking at everything
Role of omics in personalized healthcare
- Leads to
- Personalized diagnosis
- Personalized therapy
- Patient-doctor interaction
Power of omics in molecular analysis
Genomics: 3.000.000.000 DNA basis in 1 assay
Proteomics, glycomics, metabolomics: 10.000-50.000 proteins and metabolites in 1 assay
- Higher diagnostic yield
- Contextualisation of change (giving the bigger picture)
Integration of clinical omics data will drive personalized healthcare
1. Personalised diagnosis: by – omics
2. Elucidating new disease mechanisms: by deep learning, artificial intelligence, system biology
3. And monitoring new personalized (combination) therapies
Translational medicine: laboratory to society
- Using digital biomarkers
Laboratory: up to 1.000.00 signals per analysis
Society: point-of-care analysis of few biomarkers
,Lot of data: but not all date is useful data!
- Quality, not quantity
Translational innovation gaps
1. Research to research
2. Research to diagnostics
3. Research to society
So a lot of data: but half of the published data
cannot be reproduced
- Due to biological reagent and reference
materials
- Study design
- Data analysis and reporting
- Laboratory protocols
Example: biomarkers to support drug development in melanoma
- Need for blood-based biomarkers that indicate downstream effects of drug
o Inhibition of ERK pathway (pharmacodynamic)
o Tumor inhibition (efficacy)
After extensive transcriptomics profiling: IL-8 was a promising candidate biomarker
But, they also did a validation study to confirm IL-8 in melanoma to
1. Confirm elevated IL-8 in melanoma
2. Develop IL-8 assays for clinical use
- But IL-8 protein levels did not increase (significant) in plasma and serum (even though
literature confirmed this should be the case)
This was not published, because only positive outcomes are shared
- If we want to innovate clinical molecular biomarkers, we need to increase quality, not
quantity of our research
So in personalized healthcare
- Always focus on the end user: patient/ citizen
3 key aspects of personalized health(care)
1. What to measure
2. How much can it change
3. What should be the follow up for me
So, translation is key
Personal data → knowledge → understanding → decision → action
- Need for personalized data-driven GPS for health
o Monitor on background
o Alert when at risk
o Advice what to do
o Doctor as coach
And know, all this data can give a ‘funhouse mirror effect’: there is no single one reflection of health
, Tutorial: Pharmacology and drug disposition
Pharmacology: the science that is concerned with the use, effects and modes of action of chemicals
on the function of living systems
- Multidisciplinary field: molecule, cell, organ, organism, family and population
Pharmacotherapy:
- Patient
o What does the patient with the drug
o What does the drug with the patient
1. Pharmacokinetics: how does the drug travel through the body
2. Pharmacodynamics: if the drug reaches the target, how does it act?
You have both rational pharmacotherapy and evidence-based pharmacotherapy
1. Rational: mechanism-based pharmacotherapy
2. Evidence-based: does not matter how it works, as long as it works
Pharmacological phases in
pharmacotherapy
1. Exposition phase
2. Toxicokinetic phase
3. Toxicodynamic phase
Classification of pharmacological effects:
Agonist: activation of e.g. receptor → intracellular signal transduction
Antagonist: blocking of receptor → dysfunctional or no function of receptor
Classical drug-receptor interactions
1. Ligand-gated ion channels: milliseconds
2. G-protein-coupled receptors: seconds
3. Kinase-linked receptors: hours
4. Nuclear receptors: hours
- Thus, main difference = time-scale
Drug binding sites and interaction types
