Resume
Summary Pharmaceutical Medicine
- Établissement
- Katholieke Universiteit Leuven (KU Leuven)
A summary of all lectures and the course notes together. All lecture, including the guest lectures, can be found in the summary.
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Par: viridislupus • 5 mois de cela
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DRUG DESIGN AND DISCOVERYINTRODUCTION
MANAGEMENT
= phase before you can start experiments
1) Strategically: is it desirable to do?
Is there an unmet medical need in the present or future?
Eg: Alzheimer has no medical treatment
Market analysis: opportunities and risk assessment
Eg: pain killers are common, so developing a new one is not that smart
2) Scientifically/technically: can it be done?
Are there (validated) models that model the disease as present in humans?
Eg. Pfizer didn’t use the right models for their Alzheimer research => compounds
didn’t work in human
Do you go for a specific target? And does hitting this target give the wanted effect?
Which compounds can you test?
Are there patent issues with other companies?
First-in-class (never used target) or fast follower (follow up on an existing medicine)?
3) Operational: can we do it?
Do you have enough staff and expertise?
What are the costs and facilities needed?
Clinical situation Non-clinical proxy
The real situation in sick human As close as we can get
Background of the pathology We can’t try medication directly on humans => use a proxy
IF too far from real situation: compound will not work when you
go into clinical phase
OBJECTIVE OF DRUG DISCOVERY AND DESIGN
GOAL: identify pharmacologically active (hit) molecules
- For which there are clear indications
- That will reach the target in sufficient amounts such that they can exert their desired effect
- Without toxicity
FLEXIBLE
MEDICINAL
CHEMISTRY
REGULATED
1
, BIOLOGY: TARGET-BASED OR PHENOTYPIC DISCOVERY
DISCOVERY AND DESIGN RESEARCH ANTITUMORAL COMPOUNDS
EXAMPLE 1: IN VITRO ANTIPROLIFERATION ASSAY (CELLS)
= assay to find antiproliferative effect of compounds
1) 96 well plate + liquid + cells
2) Cells sink to bottom => adhere to plastic
3) Proliferation of cells
4) Add compound (eg. Stelletin) at day 0
5) Assess amount of cells quantitively and qualitatively after 3 days
= add dye/ substrate => intensity ~ amount off cells
CONCLUSION: Stelletin inhibits cell proliferation
EXAMPLE 2: IN VIVO ANTITUMORAL ASSAY (ANIMAL)
1) First check compound in vitro (exp 1) => find sensitive cell line
2) Inject cell line SC into nude mice
3) Cancer cells grow on the skin
4) Give animals compound (eg. 6OTD) or vehicle
5) Asses lump and animals
CONCLUSION 1: 6OTD inhibits cell growth => in vivo antiproliferative
CONCLUSION 2: weight is the same, mice still ate => no toxic effect
EXAMPLE 3: IN VITRO KINASE ASSAY (ENZYME)
Ligand binding => R aggregation => cytosolic enzyme
activity => TYR-P of other R (autophosphorylation) Constitutive activity Continuous “on” signal => always
=> always signal transmission signal transmission
IF: continuous transmission of EGF-R => continuous growth signal => tumor cell hallmark
1) 96 well plate + kinase substrate => substrate binds irreversible to plastic
2) Add kinase + ATP + compound
3) Phosphorylation of substrate
4) Add anti-P Ab fused with HRP
5) Assess activity of tyrosine kinase
= add HRP substrate => intensity ~ kinase activity
6) Add compound which inhibits kinase => less color
CONCLUSION: the compound inhibits tyrosine kinase activity
2
, EXAMPLE 4: IN VITRO KINASE ASSAY (REPORTER CELL LINE)
Now: high throughput screening with robots => higher speed
EXAMPLE 5: IN SILICO DRUG DESI GN (PC)
1) Crystallize the protein => idea about structure
Or: co-crystallize compound of interest to see AA interactions
2) Form 3D system of AA interactions
3) Optimize structure of the compound
COMPARANCE BETWEEN PHENOTYPIC AND TARGET BASED DRUG DISCOVERY
Phenotypic = PDD (1,2) Target based = TDD (3,4,5)
Previous focus Current focus
Limited knowledge of molecular mechanisms of disease Target: protein with key role in disease pathogenesis
BUT: still able to develop innovative medicines New tools to identify targets and interacting molecules
Current lack of success in R&D due to limited use of PDD New structure based tools to aid lead identification
BUT: current lack of success in R&D
Are there limitations of TDD?
Empirical, holistic method based on observable characteristics of Rational, hypothesis driven, molecular method based on
an organism knowledge of patho-mechanism
Testing compounds to determine if they give phenotypic changes Fundamental biomedical research -> published data -> idea of
in mammalian cells or model organisms possible target
Target-unbiased approach High throughput possible
-> don’t need to know patho-mechanistic details MoAbs-approach possible
Increased chance on first-in-class medication Intellectualy attractive
Also in vivo: indirectly also ADME tested Apply molecular/ chemical knowledge to investigate hypothesis
No prior knowledge of MoA needed Small molecule screening AND biologic based approaches
Activity in an assay is effectively translated into disease state
No knowledge of target: how to optimize structure? Validation of the target?
Often low throughput = cell is very complicated with many reactions, it
is naïve to think that targeting one reaction
Challenge of optimizing properties of candidate drugs due to lack makes everything back to normal
of design parameters provided by MoA knowledge Relevant in larger context?
Solution to hypothesis might not be relevant for disease
pathogenesis
3
, BUT: revival of interest in PDD
- REASON 1: strong contribution to first in class development
better to expend medicines on market
REASON 2: focus on TDD is a reason for the lack of success in current R&D
> difficult to identify specific molecular interactions from all potential molecular interactions
that contribute to the optimal MMOA
Key biochemical nuances needed to translate molecular interaction into
pharmacological response are missed when using TDD
> PDD has no predetermined idea about the MMOA
Aid identification of molecules with appropriate MMOA
REASON 3: can address complexity of diseases poorly understood by scientific community
REASON 4: advances in technologies for cell-based phenotypic screening
- BUT: PDD is a only a good method if the used system shares a mechanistic basis with humans
Eg. rodents develop Alzheimer, but have another mechanism as humans
BUT: many drugs defined as phenotypically found, actually used both approaches
TARGET VALIDATION
Before engaging in a new compound, you want to validate the target
- Make sure the compound will be functional (= adjust the disease)
- You want a selective inhibitor of the target protein with the right ADME characteristics
Use CRISPR-Cas9 (KO) or siRNA (KD) to decrease expression of the target protein
- Should give reversal of pathological phenotype
IF yes -> start searching for inhibitors
- Eg use siRNA to see if the loss of a target will stop tumor proliferation
CHAIN OF TRANSLATABILITY
= ability to connect observations made at different levels of biological complexity
= translation of scientific knowledge and experimental findings from basis research into drug development
Shared mechanistic basis (= construct validity) between:
- Preclinical disease model
- Assay-readout
- Human pathophysiology
BUT: many indications have no predictive animal model or phenotypic assay that corresponds to the disease
Building the chain of translatability: disease understanding
- Knowledge at molecular level of causes and drivers of disease are crucial for PDD success
> needed to select and validate exp cellular system
- Incomplete disease understanding is a limitation for validation of phenotypic models and hypothesis
driven molecular targets
4
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