Tools to aid precision treatments to prevent or manage treatment-resistant depression (TRD): Pharmacogenomics, machine learning, and artificial intelligence

Introduction

One aspirational approach to improve treatment response in major depressive disorder (MDD) and treatment-resistant depression (TRD) is to utilize biomarkers to aid accuracy of diagnosis and selection of more precise treatments ( ). One must also appreciate the role of correct diagnosis in utilizing biomarkers to improve treatment response. Biomarkers for diagnosis, such as polygenic risk scores, may also improve treatment decisions and therefore outcomes for individuals with depression and comorbid illnesses. Areas such as biomarkers in pharmacogenomics, epigenomics, proteomics, metabolomics, and microbiomics as well as algorithms such as machine learning and artificial intelligence may hold insights into matching a more effective treatment to each individual patient ( ). In this chapter, we will focus on the roles of pharmacogenomics (PGx) and algorithms to aid in treatment decisions to improve patient outcomes in MDD and TRD. Specifically, we will review the gradually improving performances of PGx tests and briefly describe progress in strategies to improve them further using artificial intelligence (AI) and machine learning (ML). We will also discuss relevant considerations for use of such tests such as optimal clinical times for administering PGx tests; recent trends in insurance coverage and federal programs for biomarker tests; and updates of FDA- drug labels for these new biomarkers. The “PGx” abbreviation in this chapter will be inclusive of both pharmacogenomic and pharmacogenetic approaches. The term “algorithm” will be inclusive of algorithmic approaches such as machine learning (ML) and artificial intelligence (AI).

Brief history of pharmacogenomic tests and algorithms for clinical use

The link between genes and mood disorders have been recognized since the Hippocratic era, but clinical approaches designed to leverage an individual’s genetic information to personalize therapy have taken centuries to develop ( ; ).

For most major medical disorders, biomarkers are demonstrated to be pivotal—even indispensable—for progress. Examples abound in oncology, cardiovascular medicine, pulmonary medicine, neurology, and other arenas ( ). The potential value of biomarker measures when treating cancers and other medical disorders gained credibility during the 1950s and helped fuel interest in PGx tests for brain-behavior illnesses. During cancer treatments, individual differences in genes and enzymes were shown to alter metabolism, clinical effectiveness and toxicity of antineoplastic medications ( ). Genetic outliers were found to have poorer clinical outcomes, sometimes because of ultrarapid metabolism requiring greater dosing, sometimes because of genetic patterns that led to poor metabolism, side effects, poor adherence, and the need for lower doses. The value of such information for treatment selections became evident, and clinical desires for biomarkers became more widespread.

Despite a growing desire for biomarkers, progress has been particularly slow in developing precision-based biomarkers to aid antidepressant selection and adjustments for the treatment of MDD, and for resolving TRD should it occur. Consequently, physicians are forced to make important clinical treatment decisions based primarily upon a collection of subjective and often erroneous clinical determinations for antidepressants such as: (1) “MDD depressions generally are the ‘same illness’ so can be treated using the same treatments”; (2) “One-size treatment tends to fit all so I will use my favorite”; and (3) “that didn’t work, so let’s try this.” These factors are common launching pads for the development of TRD. Fortunately, research advances are accelerating, and a variety of PGx tests and algorithms are beginning to show promise ( ; ).

Facilitating this acceleration is a current estimate that suggests genomic variation explains approximately 80% of the variability in drug efficacy and adverse effects for most medical illnesses ( ). Likewise, the past decade has been witness to a growing recognition that an individual’s genes play an important role in psychotropic treatment response, in part attributed to Dr. David Mrazek’s pivotal book on Pharmacogenomics ( , India slides, p. 3). He defined psychiatric pharmacogenomics as “the study of how gene variations influence the responses of a patient to treatment with psychotropic medications.” Mrazek emphasized that gene actions stemming from variations in pharmaco kinetic (PK) genes helped determine how one’s body affects the drug such as metabolizing rapidly or poorly, whereas variations in pharmacodynamic (PD) genes predominantly changed how the drug affects the body, potentially altering clinical response. Importantly, Mrazek and other investigators clarified that pharmacodynamic genes and pharmacokinetic genes interactively assert their composite actions, that medication effects are regulated by a multitude of genes and enzymes, and that most clinical decisions could be improved only by integrating and considering these composite considerations. However, most evidence in the field of pharmacogenomics relies on single gene-drug pairs and this composite or combinatorial approach is limited (PMID: 31004441) ( Fig. 7.1 ).

Evidence for pharmacogenomic testing in clinical practice

Methodological shortcomings used in nascent PGx tests and early publications were quite recognizable. Many were understandable because of absent or minimal sustained funding support, and studies were often plagued by small samples, lack of standardized clinical evaluations, limited focus upon single genes or enzymes, short durations of follow-up, no blinded ratings, and an array of other reasons. Commercial or parochial origins of emerging PGx tests for mood disorders also generated concerns ( ; ).

Efforts to minimize or overcome research shortcomings and accelerate PGx tests focus on both studies from academic/healthcare institutions and commercial PGx companies. The majority of evidence for prospective clinical utility of PGx testing has come from commercially sponsored clinical randomized controlled trials. Multiple meta-analyses have analyzed the overall effect of these studies and PGx testing efficacy in depression (PMID: 30520364).

Of note, multiple studies did show improved outcomes with PGx testing when decisions were congruent (or aligned) with the PGx test report. This indicates that individuals with gene-drug interactions may benefit the most from PGx testing ( ; ; ; ). “Congruence” generally is used to mean “in agreement,” “in harmony” or “compatible.” “Incongruence” implies its absence. While few clinicians traditionally use the terms “congruency” or “incongruency” with regard to medications, many have known about the consequences of prescribing genomically incongruent medications, i.e., those that may not be compatible with the recipient’s genomic composition. An example would be a medication for which FDA approval was based on average rates of metabolism, but when given to an individual with “incongruent” genetic composition, the results could be ineffective or undesirable. Incongruent medications for an individual commonly result in more adverse events and side effects, lower adherence, more medication discontinuations, less likelihood to achieve improved responses by failure to alter underlying, more achieving lower remission rates, and poor, disappointing outcomes. These all contribute to TRD and enable its devastating consequences.

Importantly, some experts have argued that RCTs should not be the gold standard in evaluating PGx testing because it does not mirror real clinical practice ( ; ). While open-label studies risk bias without a “placebo” standard of care arm, implementation mirrors real clinical practice. One of the largest, prospective clinical trials, Individualized Medicine: Pharmacogenetic Assessment & Clinical Treatment (IMPACT), evaluated the implementation of PGx testing in both primary care provider (PCP) settings and specialist psychiatry settings in almost 2000 patients and found that patient outcomes were actually better in individuals under the care of a PCP compared to a psychiatrist, indicating that PGx testing can be used in both specialist and nonspecialist settings ( ).

For individuals with MDD, the gold standard algorithm was when MDD treatments were compared in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. This study showed that with each medication trial, response and remission decreases while side effects increase. Interestingly, Fan et al. performed a PGx analysis simulating the STAR*D algorithm to determine potential gene–drug interactions ( ). The authors found that in the first step of STAR*D, which includes the prescription of citalopram, ~ 30% of patients had genetic implications that would change how the medication was administered. In the second step, ~ 21% had actionable gene-drug interactions. These data beg the question, that if PGx had been used to guide treatment decisions, would outcomes in STAR*D have been improved?

PGx testing to help guide medication selection appears to meaningfully help eliminate or reduce exposure to incongruent medications and to guide clinicians into avoiding their use. When making determinations about congruity vs. incongruity, PGx tests appear to more objectively integrate contributing factors, whether those be pharmacokinetic or pharmacodynamic. Consequently, should combinatorial PGx testing identify medications with greater likelihood for incongruency, that medication ideally should be avoided if possible, even should it be a “favorite” treatment for the clinician. Incongruency matters. Switching to achieve congruency matters.