Antagonists
Forms of Antagonism:
Receptor Antagonism:
Most common and specific form of antagonism.
Antagonist binds to receptor → prevents agonist from activating it.
Mechanisms:
Competitive: Competes directly with agonist at the binding site.
Non-competitive: Binds at allosteric site or blocks receptor function.
Physiological Antagonism:
Occurs when two agonists act on different receptors to produce opposing physiological effects.
Overall effect = partial or complete cancellation of each other’s actions.
Example:
Noradrenaline → ↑ heart rate via β₁-adrenoceptors
Acetylcholine → ↓ heart rate via M2 muscarinic receptors
❗No direct receptor blockade; effects are due to opposing physiological pathways.
Chemical Antagonism:
Antagonist binds directly to the agonist, forming an inactive complex.
Prevents agonist from reaching its receptor.
Does not require receptor binding by antagonist.
Example:
Protamine (positive) binds heparin (negative), which blocks the anticoagulant effect.
Antagonism of the Stimulating Messenger:
Antagonist inhibits the endogenous stimulating agent before receptor binding.
Often involves antibodies or binding proteins.
Example:
Avastin (bevacizumab) binds VEGF, which prevents receptor activation. This then blocks tumour angiogenesis.
Types of Receptor Antagonists:
Competitive Antagonists:
Bind same active site as the agonist.
Can be reversible or irreversible.
Effect: Prevent agonist binding; can be overcome by increasing agonist concentration. It is surmountable.
Sub-Types:
Reversible:
Non-covalent, transient binding
Equilibrium exists → agonist can outcompete
Surmountable: maximum effect can still be reached
Irreversible:
Covalent or very strong bond
Permanent blockade → reduces number of functional receptors
Example: Phenoxybenzamine inactivates α-adrenoceptors which leads to sustained sympathetic inhibition
Non-Competitive Antagonists:
Bind allosteric site or block receptor function (e.g., ion channels).
Effect not dependent on agonist concentration.
Mechanisms:
Allosteric modulation leads to a conformational change, which decreases agonist efficacy.
Pore blockade in ion channels prevents ion flow.
Example: Memantine is an NMDA receptor which reduces neuronal excitation.
Mechanisms of Action:
Competitive Antagonists:
Bind same site → directly block agonist.
Concentration-response curves:
Rightward shift → higher agonist concentration needed
Maximum response unchanged (Rmax) if enough agonist added
Example: Propranolol shifts formoterol (β-agonist) dose-response curve rightward.
Non-Competitive Antagonists:
Bind allosteric site or block function → reduce receptor efficacy.
Concentration-response curves:
Decreased maximum response (Rmax)
EC50 usually unchanged
Advantageous when endogenous agonist levels are high.
Concentration Response Curves for Competitive Antagonists:
Reversible competitive antagonists:
Rightward shift of agonist dose-response curve
Maximum response (Rmax) can still be reached
Example: Formoterol (β-agonist) + propranolol (β-blocker) → rightward shift in airway smooth muscle relaxation curve.
Non-Surmountable Antagonism:
Maximum response cannot be reached, regardless of agonist.
Mechanisms:
Irreversible antagonism: Covalent binding reduces receptor population
Example: Phenoxybenzamine leads to a permanent α-blockade.
Non-competitive antagonism: Allosteric binding or pore block reduces receptor efficacy
Example: Navarixin is a CXCR2 non-competitive antagonist, and has anti-inflammatory & anti-tumour properties.
Implications for Drug Development:
Therapeutic versatility:
Non-competitive antagonists reduce overstimulation, independent of agonist levels
Useful in diseases with high endogenous agonist activity
Economic considerations:
Complex molecules (antibodies) are effective but costly
Example: Avastin £21,000 per patient for six weeks → higher dose vs. macular degeneration
Functional Gaddum and Schild Analyses:
Conditions Required:
Reversible, Competitive Antagonism
The antagonist must compete directly with the agonist for the same receptor binding site.
Binding must be rapid, reversible, and at equilibrium.
The antagonist should not cause receptor conformational change or affect the response pathway itself.
Equilibrium Conditions
Measurements must be taken under steady-state conditions where agonist-antagonist-receptor interactions are stable.
Single Receptor Population
The response should be mediated by a single receptor type.
Mixed receptor types or multiple signalling pathways invalidate the analysis.
Surmountable Antagonism
Increasing the concentration of the agonist should overcome the antagonist’s effect.
A parallel rightward shift in the concentration–response curve without change in the maximum response (Rmax).
Stable and Linear Agonist-Response Relationship
The tissue or system should produce a consistent and proportional response to receptor activation.
The agonist and antagonist must reach steady-state concentrations in the tissue.
Only when these conditions are met can accurate estimates of KB, pKB, and pA2 be obtained.
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The Gaddum equation describes the quantitative relationship between the agonist and antagonist concentrations needed to produce the same level of response:
Where:
[A] = agonist concentration producing a specific response (without antagonist).
[A'] = agonist concentration producing the same response (with antagonist present).
[B] = antagonist concentration.
KB = equilibrium dissociation constant of the antagonist (affinity measure).
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The dose ratio (r) is defined as:
Substituting into the Gaddum equation gives:
When [B] = KB, then r = 2. Effectively, the antagonist concentration that doubles the agonist concentration required to produce the same effect.
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Antagonist Affinity (pA₂ and pKB)
pA2 = −logKB.
Thus:
Higher pA2 = higher affinity of the antagonist.
If the Schild slope = 1, then pA2 = pKB.
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The Schild analysis provides a graphical determination of antagonist affinity.
Measure agonist EC50 values in the presence of several antagonist concentrations.
Calculate the dose ratio (r):
3. Apply the Schild equation: log (r−1) = log[B] − logKB.
4. Plot log (r-1) (y-axis) vs. log[B] (x-axis).
