Optimizing Psychopharmacotherapy Using Personality Biomarkers: A Seven-Factor Model Perspective

Mohsen Khosravi ^1,2,3,* 

1. Department of Psychiatry, School of Medicine, Zahedan University of Medical Sciences, Zahedan, Iran

2. Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran

3. Community Nursing Research Center, Zahedan University of Medical Sciences, Zahedan, Iran

* Correspondence: Mohsen Khosravi 

Academic Editor: Armida Mucci

Received: September 19, 2025 | Accepted: January 21, 2026 | Published: January 26, 2026

OBM Neurobiology 2026, Volume 10, Issue 1, doi:10.21926/obm.neurobiol.2601320

Recommended citation: Khosravi M. Optimizing Psychopharmacotherapy Using Personality Biomarkers: A Seven-Factor Model Perspective. OBM Neurobiology 2026; 10(1): 320; doi:10.21926/obm.neurobiol.2601320.

© 2026 by the authors. This is an open access article distributed under the conditions of the Creative Commons by Attribution License, which permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is correctly cited.

1. Introduction: The Need for Personalization in Psychopharmacotherapy

The landscape of psychopharmacotherapy has evolved considerably, with significant advances in pharmacological treatments for a broad spectrum of mental disorders [1,2]. However, these advances have not eliminated the persistent challenge of pronounced interindividual variability in both drug efficacy and tolerability [3]. The prevailing reliance on a trial-and-error approach frequently results in extended periods of inadequate symptom control, adverse drug reactions, increased healthcare expenditures, and reduced patient adherence [4]. This inefficiency highlights an urgent need for more refined, individualized therapeutic strategies that can enhance clinical outcomes while mitigating associated risks [5].

Traditional efforts to personalize psychopharmacological interventions have primarily focused on demographic, clinical, and genetic factors, including age, sex, comorbid conditions, and pharmacogenomic profiles. While these parameters have improved the precision of psychiatric treatment to some extent, they do not fully account for the heterogeneity observed in patient response [6,7]. Within the broader context of precision psychiatry, tools such as pharmacogenomics, neuroimaging, and computational phenotyping have been increasingly utilized to stratify patients and tailor interventions [8,9]. Emerging research suggests that personality traits—stable patterns of cognition, emotion, and behavior—may represent a crucial yet underutilized class of biomarkers for optimizing psychopharmacotherapy [10,11,12]. Personality assessment, in this context, offers complementary information to existing precision-psychiatry modalities: whereas pharmacogenomics may identify genetic predictors of drug metabolism, and neuroimaging may reveal structural or functional brain correlates of psychiatric conditions, personality biomarkers provide insight into behavioral and affective predispositions that interact with these biological substrates to shape individual response to treatment [8,9]. Personality biomarkers, assessed through validated psychometric instruments, provide unique insights into individual neurobiological and psychological predispositions that can influence both the pharmacodynamics and pharmacokinetics of psychotropic medications [13,14,15].

Innovative psychobiological models, such as Cloninger’s model of temperament and character, offer a promising framework for exploring the neurochemical substrates underlying personality traits and their relevance to psychiatric vulnerability. Unlike symptom-focused approaches, these models emphasize causal treatment by targeting neurochemical imbalances that contribute to temperament-related vulnerabilities. Pharmacological interventions, when tailored to these underlying dispositions, have the potential to foster the development of more adaptive character traits and facilitate improved affect regulation, cognitive flexibility, and behavioral adaptation—often in synergy with psychotherapeutic modalities [14,15]. However, it is essential to acknowledge that research linking TCI dimensions to pharmacological outcomes has shown considerable variability across different disorders and methodological approaches. Inconsistent or null findings are not uncommon, reflecting the complex interplay between personality traits, psychiatric conditions, and pharmacological response. While the rationale for this approach is compelling, it is essential to acknowledge the field's current limitations. The empirical evidence supporting personality biomarkers in guiding psychopharmacological decision-making is still emerging, and most studies show only modest effect sizes, with many confounding factors such as comorbidities, environmental influences, and reporting biases yet to be fully addressed.

Despite the theoretical promise, the integration of personality biomarkers into routine psychopharmacological practice remains limited [13,14,15,16]. This perspective article seeks to articulate the scientific rationale, review the current evidence base, and discuss practical considerations for incorporating personality assessment into psychopharmacological decision-making. Crucially, it should be explicitly stated that personality biomarkers are likely to modulate, rather than replace, existing biological and computational approaches to precision psychiatry. Thus, personality assessment should be considered as a complementary tool, not as a standalone solution, for individualized treatment planning. By explicitly situating personality assessment within the larger framework of precision psychiatry, we highlight its potential to complement and enhance pharmacogenomic, neuroimaging, and computational phenotyping approaches, thereby fostering more holistic and nuanced patient stratification for individualized treatment planning [8,9]. By advancing this paradigm, we advocate for a more nuanced, personalized, and ultimately more effective approach to psychiatric treatment that transcends traditional boundaries. Nonetheless, the use of personality-based stratification in treatment planning also introduces ethical and practical challenges. There are risks that a misapplied personality profile could lead to stigmatization or inappropriate labeling of patients, and potentially to the misuse of sensitive personal data. These concerns underscore the necessity for careful ethical reflection and robust safeguards, which will be discussed below.

2. Understanding Cloninger’s Seven-Factor Model: Definitions and Current Evidence

The integration of personality biomarkers into psychopharmacotherapy promises to revolutionize psychiatric care by enabling individualized treatment approaches. Central to this endeavor is the Seven Factor Model, as detailed by Cloninger and colleagues, which conceptualizes personality as comprising two interacting components: temperament and character. Temperament represents the biological core of personality, encompassing four dimensions—harm avoidance, novelty seeking, reward dependence, and persistence—that are rooted in early associative learning and reflect stable, heritable behavioral dispositions. These traits are closely aligned with the constructs of intermediate phenotypes or endophenotypes, given their strong biological and potential genetic underpinnings. In contrast, character, composed of self-directedness, cooperativeness, and self-transcendence, emerges later through conceptual and insight learning, is more susceptible to cultural and environmental influences, and matures over time as neural plasticity shapes adaptive behavior [14,15,17]. Table 1 summarizes the subscales of the Temperament and Character Inventory (TCI), providing descriptions of high and low scores for each subscale, as well as outlining how different types of reinforcement influence emotional states along these dimensions [18].

Table 1 The Temperament and Character Inventory (TCI) subscales, high/low score descriptions, and reinforcement effects on emotional state.

The interplay between temperament and character is both etiological and developmental: temperament traits may serve as precursors or substrates for the development of character, while mature character traits can, in turn, modulate the salience and interpretation of affective and perceptual experiences governed by temperament [19,20]. Table 2 highlights key distinctions between temperament and character, including their developmental trajectories, neurobiological correlates, and responsiveness to environmental input [15,17]. These differences underscore the utility of targeting specific personality components in psychopharmacological interventions; for example, temperamental traits may predict pharmacological response or vulnerability to side effects, while character traits could inform psychoeducational or adjunctive behavioral strategies [14].

Table 2 Main distinctions between temperament (associative/procedural learning) and character (conceptual/semantic learning).

Measurement of these constructs is operationalized through the TCI, a psychometrically robust instrument with various versions tailored for age, informant type, and assessment detail [21]. The TCI’s multidimensional structure allows for precise mapping of an individual’s personality profile, thereby offering a potential framework for stratifying patients and optimizing medication selection and dosing. For instance, individuals scoring high on harm avoidance may benefit from anxiolytic strategies, whereas those with low reward dependence might require interventions targeting social motivation. Crucially, personality assessment as operationalized by TCI can be integrated with other precision-psychiatry methods: for example, combining TCI-derived trait profiles with pharmacogenomic data or neuroimaging biomarkers could yield multidimensional patient subgroups with more homogeneous treatment response patterns [8,9]. However, it should be critically noted that while the TCI demonstrates acceptable reliability and validity in many contexts, concerns remain regarding its cross-cultural applicability, temporal stability, and susceptibility to self-report or clinician biases. Differences in cultural norms, language translation, and respondent interpretation may affect the reliability of personality assessment across diverse populations. Furthermore, personality traits, while relatively stable, can fluctuate over time or in response to significant life events, complicating their use as static biomarkers for long-term treatment planning.

Ultimately, the Seven Factor Model and TCI provide a theoretically grounded and empirically validated basis for incorporating personality biomarkers into personalized psychopharmacotherapy, paving the way for more effective, tolerable, and patient-centered treatment regimens [14,15,18]. Additionally, the potential for misuse of personality data—such as stereotyping or the creation of self-fulfilling prophecies—necessitates careful data governance and clinician training to ensure that personality information is used constructively and not as a basis for limiting patient opportunities or autonomy.

3. Linking Personality Traits to Psychopharmacotherapy Outcomes

3.1 Overview of Psychobiological Models

Recent advances in psychobiological models of personality offer compelling frameworks for optimizing psychopharmacotherapy by targeting the neurochemical substrates of temperament traits, rather than merely alleviating symptoms [14]. Unlike factor-analytic models, psychobiological approaches provide testable hypotheses regarding the pharmacological modulation of trait vulnerabilities underpinning major mental disorders [14,15]. This paradigm shift aims for causal interventions—correcting the neurophysiological predispositions that manifest as extreme temperament—thereby setting a more favorable neurobiological stage for character development and adaptive functioning, especially when combined with expert psychotherapy or transformative personal experiences [14,15,18].

3.2 Neurochemical Correlates of Temperament Traits

Central to this approach is the mapping of distinct temperament traits to specific neurophysiological circuits and neurotransmitter systems within the central nervous system (CNS) [22,23,24,25,26]. For example, harm avoidance is primarily linked to GABAergic and serotonergic activity, whereas novelty seeking is associated with dopaminergic function. Reward dependence involves serotonergic and noradrenergic systems, and persistence involves the glutamatergic pathway (see Table 3 for a comprehensive review of empirical evidence [27]). These associations enable tailoring pharmacological treatments to the individual’s trait-related neurochemical profile.

Table 3 Four central brain systems related to personality dimensions, their principal neuromodulators, relevant stimuli, and typical behavioral responses.

3.3 Limitations and Moderating Factors

However, the strength and consistency of these trait-treatment associations vary across disorders and studies. While some research supports clear links between TCI dimensions and drug response, other investigations yield null or contradictory results, suggesting that additional moderating factors—such as comorbidities, illness severity, and environmental context—may influence outcomes. It is therefore essential to interpret these associations with caution, as the neurochemical underpinnings of personality are complex and multidetermined, involving overlapping and interacting pathways. Current clinical applications based on these mappings remain largely heuristic and require further validation in extensive, prospective, randomized studies [27].

3.4 Personality Profiles Across Psychiatric Disorders

Personality profiles vary across psychiatric disorders, as demonstrated in Table 4, with each disorder displaying distinct elevations or reductions in specific traits [28,29,30,31]. For instance, high harm avoidance is a common feature in conditions such as major depressive disorder, anxiety disorders, and obsessive-compulsive disorder. Patients with elevated harm avoidance may benefit more from anxiolytic medications due to their heightened sensitivity to stress and anxiety [14]. Conversely, individuals with high novelty seeking, as observed in bipolar disorder, borderline, and antisocial personality disorders, may respond more favorably to antipsychotics and mood stabilizers, which help regulate impulsivity and affective instability [14,18,32]. Elevated reward dependence, seen in histrionic and dependent personality disorders, suggests a potential advantage for medications like serotonin-norepinephrine reuptake inhibitors, which can enhance social motivation and reward processing [33,34]. Persistence, when increased, as in anorexia nervosa and obsessive-compulsive personality disorder, may necessitate the use of N-methyl-D-aspartate receptor modulators to address cognitive rigidity and perseverative behaviors [35,36]. Furthermore, low levels of self-directedness and cooperativeness, commonly noted in various personality disorders, indicate a greater need for structured psychotherapy interventions aimed at improving self-regulation and interpersonal functioning [37,38,39].

Table 4 Personality traits in mental disorders.

3.5 Summary

By targeting the neurophysiological systems that regulate affect, these interventions are hypothesized to reduce maladaptive biases in affective and cognitive processes, ultimately facilitating conceptual learning and improvement in both behavioral and mental symptoms. Nevertheless, it should be emphasized that the associations between personality profiles, psychiatric diagnosis, and medication response are not universally observed; some studies report null or even opposite findings, further highlighting the need for cautious interpretation and additional research. While many proposed causal strategies require further empirical validation, their grounding in behavioral neuroscience promises a more direct and personalized alignment between psychiatric diagnosis and treatment. In the context of precision psychiatry, integrating personality assessment with pharmacogenomic and neuroimaging data could help explain interindividual differences in drug response and side-effect profiles observed even within genetically or neurobiologically similar subgroups, thereby filling a critical gap left by other biomarker approaches [8,9]. To illustrate the potential value of personality-informed psychopharmacological decision-making, consider the following clinical vignette: a patient with major depressive disorder and high harm avoidance may demonstrate heightened anxiety and sensitivity to side effects with standard selective serotonin reuptake inhibitors (SSRIs). In such a case, the clinician might consider initiating treatment with a lower dose of SSRI, closely monitoring for side effects, and integrating supportive psychotherapy to address underlying temperament vulnerabilities. Alternatively, a patient with bipolar disorder and high novelty seeking may require careful titration of mood stabilizers, with particular attention to impulsivity and risk-taking, potentially benefiting from adjunctive behavioral interventions targeting these domains. These examples underscore how incorporating personality assessment can guide both medication selection and adjunctive treatment planning, enhancing patient outcomes relative to standard, non-stratified approaches [14,15,18]. It is also crucial to acknowledge that using personality-based treatment recommendations could inadvertently reinforce existing biases or stereotypes about certain patient groups. Care must be taken to ensure that personality information is applied in a supportive and empowering manner, rather than in a restrictive or pejorative manner.

4. Challenges and Limitations in Using Personality Biomarkers

The use of personality biomarkers to optimize psychopharmacology holds significant promise, yet it faces numerous challenges and limitations that constrain its clinical utility. First, personality is a complex, multidimensional construct influenced by genetic, environmental, and developmental factors, making it difficult to distill into discrete, measurable biomarkers [40]. The heterogeneity of mental disorders further complicates the identification of reliable biomarkers, as symptom overlap and comorbidities can blur the relationship between personality traits and pharmacological responses [41,42]. Additionally, research in this area often produces inconsistent or null findings, with some studies failing to replicate previously reported associations between personality dimensions and drug response. This variability may be attributable to differences in study design, assessment tools, diagnostic criteria, sample characteristics, and analytic approaches. Current methodologies for assessing personality biomarkers—ranging from neuroimaging to genetic and epigenetic profiling—often yield inconsistent or non-reproducible results due to small sample sizes, population stratification, and methodological variability. Moreover, many biomarker studies lack longitudinal data, limiting the understanding of how personality traits and their biological correlates change over time and in response to medication.

Psychometric limitations of personality assessment tools must also be considered. Measures such as the TCI may be influenced by cultural background, language, and social desirability bias, potentially affecting their validity across diverse populations. Temporal stability, while generally acceptable, is not absolute, and personality trait measures may be sensitive to significant life events or fluctuations in psychiatric symptoms [14,15,18]. Ethical and privacy concerns also arise when utilizing sensitive biological data to guide treatment, potentially leading to stigmatization or discrimination. For instance, integrating personality data into clinical records could expose patients to risks such as insurance discrimination, employment bias, or unwarranted labeling. Beyond concerns about privacy and stigma, there are further practical risks: personality-based stratification may inadvertently lead to labeling or rigid categorization of patients, potentially influencing clinicians’ expectations and decision-making in ways that are not always beneficial. Misuse or overreliance on personality profiles could result in reduced treatment options, restriction of patient autonomy, or reinforcement of existing biases within mental health services. To mitigate these risks, robust safeguards must be implemented, including strict data privacy protocols, informed consent procedures, and clinician training to avoid stigmatizing interpretations of personality assessments. Furthermore, ethical guidelines should explicitly address the responsible use of personality information in psychiatric practice, with mechanisms for oversight and patient advocacy [43].

Additionally, the translation of biomarker findings into clinical practice is hindered by the absence of standardized protocols and regulatory frameworks, as well as limited clinician training in interpreting and applying such complex data [18]. Pharmacogenetic approaches, while promising, often fail to account for the dynamic interplay between personality, environment, and other psychosocial variables [44,45]. Likewise, neuroimaging and computational phenotyping approaches, though valuable, may not capture the subjective, dispositional aspects of behavior that personality assessment contributes, underscoring the need for integrative multimodal strategies [8,9]. Furthermore, most research to date has focused on adult populations of European descent, raising concerns about generalizability and equity in mental health care. There are also methodological inconsistencies in the design and reporting of biomarker studies, including variations in sample selection, diagnostic criteria, and analytic techniques. These inconsistencies limit the generalizability and reproducibility of findings. Additionally, integrating personality data with genetic and neuroimaging markers poses technical and interpretative challenges, as the relationships among these domains are complex and not fully understood. Interdisciplinary collaboration among psychiatrists, geneticists, neuroscientists, and ethicists is essential to address these challenges and ensure the responsible advancement of the field. The integration of personality biomarkers into psychopharmacology therefore necessitates interdisciplinary collaboration, larger and more diverse cohorts, rigorous validation studies, and the development of robust ethical guidelines. Ultimately, a careful and ethically informed approach is essential to ensure that the potential benefits of personality-informed psychopharmacotherapy are realized while minimizing the risk of harm through misuse, labeling, or discrimination.

5. Future Directions: Towards Precision Psychiatry

Future research on optimizing psychopharmacotherapy with personality biomarkers is poised for several promising directions. First, advances in biomarker discovery, including molecular, neurochemical, and physiological indicators, are expected to enhance the precision of psychiatric diagnoses and individualized treatment plans. As technology evolves, integrating these biomarkers with genetic and neuroimaging data will provide a more comprehensive understanding of the biological underpinnings of personality traits and their relationship to drug response. This multidimensional approach can improve the accuracy of predicting treatment efficacy and side effect profiles. Computational phenotyping—leveraging large-scale digital and behavioral data—can further augment personality assessment, enabling dynamic, real-time monitoring of patient traits and responses within precision psychiatry frameworks [8,9]. Moreover, the growing application of artificial intelligence (AI) and machine learning algorithms holds significant potential for predicting treatment outcomes. By analyzing large-scale datasets that combine personality assessments, biomarkers, genetic profiles, and neuroimaging results, AI can identify complex patterns and interactions that may be undetectable through conventional statistical methods [46]. This can lead to the development of personalized psychopharmacological interventions tailored to an individual’s unique biological and psychological makeup. To advance the field, future research should prioritize large-scale, prospective, and longitudinal study designs such as randomized controlled trials and cohort studies involving diverse populations. Standardizing assessment protocols and analytic methods, including cross-cultural validation of personality measures and integration with multi-modal biomarker data, is crucial. AI-driven integrative models should be leveraged to address the complexity of interactions among personality, genetic, and neuroimaging variables. Collaborative, interdisciplinary research teams are essential to ensure methodological rigor, ethical oversight, and translational relevance. In addition, concrete clinical implementation studies—demonstrating how personality-informed psychopharmacotherapy compares to standard care in real-world settings—are needed to bridge the gap between theory and practice. Ultimately, the synergy of personality biomarkers with pharmacogenomic, neuroimaging, and computational phenotyping approaches is likely to define the future of truly individualized psychiatric care [8,9].

6. Conclusions

Incorporating personality biomarkers, particularly through the Seven-Factor Model, represents a transformative step toward precision psychopharmacotherapy. By moving beyond conventional demographic and genetic stratification, this approach recognizes the profound impact of temperament and character traits on both pharmacological response and psychiatric vulnerability. However, it is essential to highlight that associations between personality dimensions and specific neurochemical pathways should be regarded as heuristic frameworks, reflecting complex, distributed, and interacting systems rather than direct one-to-one correspondences. This perspective enables clinicians to tailor treatments more effectively, fostering improved symptom control, tolerability, and patient adherence. Nonetheless, significant challenges remain, including methodological variability, limited generalizability, and ethical concerns surrounding biomarker use. It is also vital to recognize that existing research demonstrates considerable heterogeneity in findings linking TCI dimensions to treatment outcomes; thus, current recommendations should be considered preliminary and interpreted with appropriate caution. Therefore, claims regarding the clinical utility of personality biomarkers must be viewed as provisional, pending further research, and oversimplification should be avoided. Advancing this paradigm requires robust interdisciplinary collaboration, diverse cohort validation, and the development of clear clinical guidelines. As technological innovations—such as AI-driven analytics and integrative biomarker platforms—continue to evolve, integrating personality assessment into routine practice holds great promise for realizing truly individualized, effective, and patient-centered psychiatric care. Positioning personality assessment as a complementary tool alongside pharmacogenomics, neuroimaging, and computational phenotyping ensures conceptual alignment with the broader aims of precision psychiatry. Ultimately, embracing a nuanced and integrative approach to personality biomarkers may define the next frontier in precision psychiatry.

Author Contributions

Mohsen Khosravi: Conceptualization, Writing – original draft, Writing – review & editing.

Competing Interests

The author declares that he has no known competing financial interests or personal relationships that could have influenced the work reported in this paper.

AI-Assisted Technologies Statement

AI was used only to translate and correct a few phrases from Persian to English using Platonia (https://platonia.co/); no AI-generated text appears in the manuscript.