Pharmacodynamic Algorithmic Methodology

Rationale

Pharmacodynamic pharmacogenetics for psychotropic medications has not yet reached the same level of actionable evidence as pharmacokinetic testing. The literature also contains inconsistent and, at times, contradictory findings regarding the influence of individual receptors, transporters and enzymes on medication response.

One possible explanation is methodological. Much of the earlier research examined the association between a single genetic variant and clinical response to a medication. However, psychotropic drug response is now widely recognized as polygenic, reflecting the combined influence of multiple receptors, transporters, enzymes and regulatory pathways.

It is therefore unrealistic to expect a single genetic variant, considered in isolation, to consistently predict whether a medication will succeed or fail. A more informative approach may be to evaluate the collective genetic characteristics of the biological pathway through which a drug acts and determine whether that pathway appears more or less favourable for the medication in a particular patient.

This is the rationale underlying Personalized Prescribing’s mechanism-based, multigene approach to pharmacodynamic assessment.

Rather than relying on isolated pharmacodynamic variants as deterministic predictors of response, each medication is modelled according to its known mechanism of action. Functional genetic variants affecting the drug’s primary targets and relevant supporting pathways are then integrated using evidence-weighted algorithms to estimate relative pharmacodynamic suitability.

Importantly, this drug-specific pharmacodynamic assessment is maintained separately from three other dimensions of the Personalized Prescribing model:

• pharmacokinetic exposure;
• genetically inferred basal neurotransmission capacity; and
• the patient’s current symptom-derived functional state.

The final clinical interpretation therefore considers not only whether the patient is likely to achieve appropriate drug exposure, but also whether the medication’s biological targets appear favourable, whether the relevant neurotransmitter pathways have adequate inherited capacity, and whether the medication’s mechanism is aligned with the patient’s current clinical needs.

A Useful Analogy: Predicting the Performance of an Automobile

Consider predicting whether an automobile will perform adequately.

Its performance cannot be determined by examining a single component. The engine, transmission, tires and other systems all contribute, but they do not contribute equally.

The engine is fundamental. If a critical engine component is severely impaired, the vehicle may perform poorly regardless of how well other systems function.

The transmission is also essential. A strong engine cannot fully compensate for a severely impaired transmission.

The tires remain important for traction and overall performance, but their contribution is different and should therefore carry less weight than the engine or transmission.

The same principle applies to psychotropic pharmacodynamics.

A medication’s response is unlikely to depend on a single gene or receptor. Instead, it depends on several biological targets and pathways that differ in their importance to the drug’s mechanism of action.

Personalized Prescribing therefore evaluates multiple drug targets and relevant genetic variants, assigning greater weight to those that are more important to the medication’s mechanism and more strongly supported by scientific evidence.

The objective is not to ask whether one genetic variant predicts response, but whether the overall biological pathway through which the medication must act appears relatively favourable or unfavorable.

A Simplified Example: The Pharmacodynamics of an SSRI

SSRI response is unlikely to depend on a single genetic variant. It reflects the combined function of several components within the serotonergic pathway.

A simplified model can begin with three important targets:

SLC6A4: The Serotonin Transporter (SERT)

SERT is the primary target of SSRIs. Its role is to remove serotonin from the synapse for recycling.

Higher-expression SLC6A4 profiles may provide greater transporter availability for the drug to inhibit, while lower-expression profiles may provide fewer transporter targets. However, SLC6A4 alone does not determine SSRI response.

HTR1A: The Presynaptic “Brake”

The 5-HT1A autoreceptor helps regulate serotonin release. When synaptic serotonin rises following SERT inhibition, HTR1A autoreceptors reduce serotonergic firing and serotonin release.

Variants associated with stronger autoreceptor activity may therefore oppose the intended increase in serotonergic signaling, while more typical regulation may be more favourable.

HTR2A: The Postsynaptic “Antenna”

Increasing serotonin in the synapse is only useful if the signal can be transmitted effectively.

HTR2A is an important postsynaptic serotonin receptor involved in downstream signaling. Reduced receptor expression or signaling may weaken serotonergic transmission, while excessive signaling may contribute to activation-related adverse effects in susceptible patients.

Considering the Pathway as a Whole

These receptors illustrate why a single genetic variant should not be used as a deterministic predictor of SSRI response.

Conceptually:

• Favorable SERT function + favorable HTR1A regulation + favorable HTR2A signaling may increase confidence in SSRI pharmacodynamic suitability.
• Lower SERT availability + stronger HTR1A inhibition + reduced HTR2A signaling may reduce confidence in an adequate SSRI response.
• Mixed combinations may produce intermediate results.

The components should not be weighted equally. Greater weight should be assigned to targets that are more important to the drug’s mechanism and to variants supported by stronger functional and clinical evidence.

Personalized Prescribing therefore does not ask whether a single gene predicts SSRI response. Instead, it evaluates whether the combined biological pathway through which the SSRI must act appears relatively favorable or unfavorable for that patient.

This drug-specific pharmacodynamic assessment is then considered separately from pharmacokinetic exposure, genetically inferred basal serotonergic capacity and the patient’s current symptom-derived functional state.

Together, these distinct dimensions provide a more structured basis for estimating relative medication suitability while preserving the role of clinical judgment in final treatment decisions.