Week 1 pt 2 Flashcards
Bioassays
A measurement of the potency of a drug by measuring the magnitude of biological response it produces
- includes efficacy and toxicity
- predicts likely behaviour of the
drug in a clinical setting - normally has a comparison of the drug compared to a standard
- Qualitative vs quantitative
measurements - Need positive & negative controls
- High degree of variability (error)
Organ bath bioassays
- add known conc of drug to a tissue and measure response
- historically used commonly in pharmacology
Drug discovery
identification of lead drugs
Drug development (in vitro & in animals)
High throughput screening of many compounds to identify useful drug candidates using automated systems or computational models
Molecular modelling/simulations
- understand how drugs dock with their targets
- model protein binding sites
- use this information to develop ‘better’ drugs
In silico and in vitro assays to test compounds:
- efficacy
- toxicity
- pharmacokinetics (ADME)
- solubility/membrane permeability
- limit use of animals
Aim to identify lead ‘hit’ compounds
Clinical studies (in humans)
- Phase I (safety & pharmacokinetics)
- Phase II (efficacy & more detailed safety)
- Phase III (large studies comparing new drug to existing drugs)
Post marketing studies
- Detect rare or long term adverse effects not identified in clinical trials
- Eg. AstraZeneca Covid vaccine can cause blood clots in females
- Sometimes result in drug withdrawal from market
in silico vs in vitro
in vitro:
human or animal studies,
experiments study digestion outside the body - biological
in silico:
experimentation performed by computer - rather than biological
Quantitative in vitro assays (eg)
- Flow cytometry (FACS)
- Enzyme Linked
ImmunoSorbent Assay
(ELISA) - Cytotoxicity
- Organoid studies
- ‘Organ on a chip’
- Healthy animals
(pharmacokinetics/toxicity) - Disease models (drug efficacy)
- Genetic models (eg)
- Cystic fibrosis
- Muscular dystrophy
- Alzheimers
- Parkinsons
- Humanised mouse models
- Disease model should reflect the
clinical situation (eg) - respiratory disease models (rodents vs sheep/pigs)
- rodent tumours models (xenograft vs human explant)
Human clinical trials
- Final demonstration of safety, efficacy and in vivo behaviour
- Many drugs fail in early clinical trials
- Compare outcomes with a known/used drug/standard (controls)
- Endpoint can be physiological, subjective, long term or quality of live
Human trials:
Parallel group design
Group A receive the treatment,
Group B receive the control
Crossover design
Both group A and B receive the treatment AND the control
Randomised controlled trial (avoidance of bias)
Blinded assignment of trial subjects to groups (neither the
investigators nor subjects know what they are being given)
Sample size
- Determined statistically BEFORE the study commences
(financial and ethical considerations) - Power calculation (how many subjects do you need to
obtain X outcome?)
Sample error
- Type I (difference between A and B where there is none –
significance) - Type II (no difference observed when there is one – power)
Ethics
Animals
* Is there good justification that the work needs to be done?
* Has every attempt been made to limit or avoid suffering?
* Does the potential benefit of the study outweigh the potential adverse effects on the
animal?
* Has every attempt been made to avoid or minimise the use of animals?
Humans
* Needs to be evidence of efficacy and safety in animals first
* Humans are more complex than animal models
* Scientific importance need to be demonstrated
* Must include females and males (unless intended for one or the other)
* Must not compromise human treatment/care
* Must obtain full informed consent (subject is fully aware of the risks and design of
the trial)
* Must ensure the study is sufficiently powered
You are never allowed to use in analytics more animals than is fundamentally required and you are not allowed to expose animals to certain tests that are going to be unethical.
