The More, the Merrier…? Antipsychotic Polypharmacy Treatment Strategies in Schizophrenia From a Pharmacology Perspective

Abstract

Antipsychotic polypharmacy/drug combination treatment (APP) is a remarkably common practice in the schizophrenia context, given the lack of general support in treatment Guidelines. There is also a vast literature on APP outcomes, but a paucity of high-quality evidence-based data to guide and optimize adequate use of APP. This seems particularly true regarding many pharmacology-based considerations involved in APP treatment strategies. This paper first briefly summarizes clinical literature related to the use of APP. Against this backdrop, the pharmacological target profile features are then described of frequently used antipsychotic agents, in relation to estimated free plasma exposure levels at clinically efficacious dosing. APP strategies based on the properties of these drugs are then scrutinized and gauged within the background literature framework. The anticipated usefulness of APP from the pharmacological standpoint is detailed regarding efficacy, adverse effect (AE)/tolerability, and safety perspective, including why, when, and how it may be used to its advantage. For the purpose, a number of theoretically beneficial combinations as well as instances with suboptimal-and even futile-APP approaches are exemplified and discussed from the rational pharmacodynamic and pharmacokinetic pros and cons point-of-view. In this exposé, particular attention is paid to the utility and features of 3rd Generation Antipsychotic dopamine (DA) D2-D3 agonists within an APP setting.

Keywords: adverse events; antipsychotics; drug combinations; efficacy; pharmacodynamic profiles; polypharmacy; pros and cons; schizophrenia.

Figure 1

Antipsychotic polypharmacy (APP)—broad literature background impressions.

Figure 2

Stepwise transition to APP, based on NICE, UK, Clinical Guidelines (5).

Figure 3

Commonly given pharmacodynamically (PD)-based reasons to initiate APP.

Figure 4

Target and associated AE profiles of HAL, OLA, RIS, ARI, and CAR. Colored lines and dots represent drug profiles based on the 8 different targets depicted at the edges of the cobweb; affinity is highest at the center, lowest at the edges (0.1–10,000 nM log scale). Dot line-enclosed shaded areas in the center represent unbound plasma levels of the compounds at efficacious therapeutic dosing. Circles and ovals depict targets likely to be affected at these levels, with colors indicating desired (yellow), accessory beneficial (light blue), and unwanted effects (red; + text below graphs). Red ovals in ARI and CAR graphs pinpoint D2 and D3 affinities. EPS, Extrapyramidal side effects; PRL, prolactin (rise). The drug cobweb profiles in this figure and Figures 5, 6 were compiled from data in public web databases and complementary literature, including drug SPC's; viz. human (cloned or native tissue) receptor affinities (–24); therapeutic steady-state exposures (25); free fraction of drug plasma concentrations (26). Therapeutic steady-state exposure areas shown were obtained by converting ng/mL (25) to nM, and multiplying by the free fraction in plasma (26) for the corresponding drug.

Figure 5

Some potentially useful APP target combinations based on FGA, SGA, and TGA. Shown are target profiles of APP combination examples based on CLZ, OLA, QUE, ARI, and CAR, displayed in a cobweb design. The colored lines joining the dots represent the target profile of the agent with corresponding color coding; e.g., blue is CLZ, and green is CAR. The dot line-enclosed shaded areas represent unbound plasma concentrations of the compounds at therapeutic dosing. For example, in the OLA + CAR case, the yellow and pink areas show unbound OLA and CAR plasma concentrations, respectively. For further explanations, see legend to Figure 4.

Figure 6

Some theoretically suboptimal, less preferable—or even futile—APP target combination examples based on FGA, SGA, and TGA. Shown are target profiles of APP combination examples based on CLZ, RIS, HAL, CAR, QUE, OLA, and ARI displayed in a cobweb design. For further explanations, see legend to Figures 4, 5.

Figure 7

Data from a case report of CAR add-on to CLZ treatment. Shown is a brief schizophrenia treatment history of a female and a male patient prior to APP add-on with CAR to ongoing CLZ treatment. The accompanying graphs illustrate PANSS scoring and BMI observations evolving over time following start of the CAR add-on intervention [data extracted from De Berardis et al. (16)].