A Southeast Asia Consensus on the Definition and Management of Treatment-Resistant Depression

Why the irremediability requirement is not sufficient to deny psychiatric euthanasia for patients with treatment-resistant depression

Treatment-resistant depression (TRD) holds centrality in many debates regarding psychiatric euthanasia. Among the strongest reasons cited by opponents of psychiatric euthanasia is the uncertainty behind the irremediability of psychiatric illnesses. According to this argument, conditions that cannot be considered irremediable imply that there are possible remedies that remain for the condition. If there are possible remedies that remain for the condition, then patients with that condition cannot be considered for access to euthanasia. I call this the irremediability requirement (IR). I argue that patients with TRD can, indeed, meet the operationalisation of irremediability in the IR. This is because the irremediability it asks for is not some global or absolute irremediability, but rather a present irremediability based on the current state of medical science. I show this by considering irremediability relating to (1) possible future treatments and (2) not trying presently available alternative treatments. I extend Schuklenk nd van de Vathorst's argument from parity to terminal malignancies, to show that (1) is an unreasonable expectation for all cases of euthanasia. Taking (2) as a more serious opponent to psychiatric euthanasia, I show how the IR, based on how it is presently operationalised, can be realistically applied to cases of TRD. I do this by further developing Tully's argument on broad-sense treatment resistance with the robust empirical data from the STAR*D trials. If my argument from Tully's is valid, then we have reasons to, again, seek parity between the operationalisations of irremediability in terminal malignancies and TRD.

Personalized Approach in the Management of Difficult-to-Treat and Treatment-Resistant Depression With Second-Generation Antipsychotics: A Delphi Statement

Background Major depressive disorder (MDD) has many facets including mixed or atypical depression that requires personalized care to improve treatment-related outcomes. Second-generation antipsychotics (SGAs) offer complementary mechanisms for clinical roles in difficult-to-treat depression and treatment-resistant depression cases. Aim/objective To further delineate a consensus on the clinical positioning of SGAs for MDD, mixed, or atypical depression, a Knowledge Attitude Perception (KAP)-mediated Delphi Statement was planned. Material/methods A literature review for the definition, diagnosis, and management of MDD, mixed, and atypical depression as treatment-resistant depression (TRD) or difficult-to-treat depression (DTD) was conducted by a steering committee of academic and clinical experts (n=6) while developing a validated KAP questionnaire. Scientific statements as clinical recommendations were evolved using the Delphi methodology before building a clinical expert consensus with an online survey (n=24). Results Twenty-four psychiatrists highlighted DTD to offer a multidimensional approach to assess treatment strategies involving selective serotonin reuptake inhibitors (SSRIs) or SGAs, while ensuring symptom, functional, and quality of life (QoL) domain improvement for improved outcomes and remission rates. MDD cases with anxiety, anhedonia, comorbidities, and risk traits require personalized care with early induction of SGAs for severe cases or symptom persisters with functional impairment. Early augmentation with SGAs including aripiprazole or cariprazine can provide a favorable risk-benefit profile for clinical cases of MDD with or without the antecedent of mixed depression or personality disorder.  Conclusion The literature review and KAP responses emphasize the importance of early identification for personalized care strategies with SGAs for DTD. Large-scale real-world evidence needs to evolve with due recognition of different phenotypes as TRD or DTD with partial or functional impairment to understand the impact of appropriate treatment pathways with SGAs.

Escitalopram Personalized Dosing: A Population Pharmacokinetics Repository Method

Escitalopram (SCIT) represents a first-line antidepressant and antianxiety medication. Pharmacokinetic studies of SCIT have demonstrated considerable interindividual variability, emphasizing the need for personalized dosing. Accordingly, we aimed to create a repository of parametric population pharmacokinetic (PPK) models of SCIT to facilitate model-informed precision dosing. In November 2022, we searched PubMed, Embase, and Web of Science for published PPK models and identified eight models. All the structural models reported in the literature were either one- or two-compartment models. In order to investigate the variances in model performance, the parameters of all PPK models were derived from the literature published. A representative virtual population, characterized by an age of 30, a body weight of 70 kg, and a BMI of 23 kg/m2, was generated for the purpose of replicating these models. To accomplish this, the rxode2 package in the R programming language was employed. Subsequently, we compared simulated concentration-time profiles and evaluated the impact of covariates on clearance. The most significant covariates were CYP2C19 phenotype, weight, and age, indicating that dosing regimens should be tailored accordingly. Additionally, among Chinese psychiatric patients, SCIT showed nearly double the exposure compared to other populations, specifically when considering the same CYP2C19 population restriction, which is a knowledge gap that needs further investigation. Furthermore, this repository of parametric PPK models for SCIT has a wide range of potential applications, like design miss or delay dose remedy strategies and external PPK model validation.