The current understanding of precision medicine and personalised medicine in selected research disciplines: study protocol of a systematic concept analysis

Personalized assessment and management of non-specific low back pain

BACKGROUND AND OBJECTIVE

Low back pain (LBP), and in particular non-specific low back pain (NSLBP), which accounts for approximately 90% of LBP, is the leading cause of years lived with disability worldwide. In clinical trials, LBP is often poorly categorized into 'specific' versus 'non-specific' and 'acute' versus 'chronic' pain. However, a better understanding of the underlying pain mechanisms might improve study results and reduce the number of NSLBP patients.

DATABASES AND DATA TREATMENT

Narrative review.

RESULTS

NSLBP is a multi-dimensional, biopsychosocial condition that requires all contributing dimensions to be assessed and prioritized. Thereby, the assessment of the contribution of nociceptive, neuropathic and nociplastic pain mechanisms forms the basis for personalized management. In addition, psychosocial (e.g. anxiety, catastrophizing) and contextual factors (e.g. work situation) as well as comorbidities need to be assessed and individually weighted. Personalized treatment of NSLBP further requires individually choosing treatment modalities, for example, exercising, patient education, cognitive-behavioural advice, pharmacotherapy, as well as tailoring treatment within these modalities, for example, the delivery of tailored psychological interventions or exercise programs. As the main pain mechanism and psychosocial factors may vary over time, re-assessment is necessary and treatment success should ideally be assessed quantitatively and qualitatively.

CONCLUSIONS

The identification of the main contributing pain mechanism and the integration of the patients' view on their condition, including beliefs, preferences, concerns and expectations, are key in the personalized clinical management of NSLBP. In research, particular importance should be placed on accurate characterization of patients and on including outcomes relevant to the individual patient.

SIGNIFICANCE STATEMENT

Here, a comprehensive review of the challenges associated with the diagnostic label 'non-specific low back pain' is given. It outlines what is lacking in current treatment guidelines and it is summarized what is currently known with respect to individual phenotyping. It becomes clear that more research on clinically meaningful subgroups is needed to best tailor treatment approaches.

Individualisation, personalisation and person-centredness in mental healthcare: a scoping review of concepts and linguistic network visualisation

BACKGROUND

Targeted mental health interventions are increasingly described as individualised, personalised or person-centred approaches. However, the definitions for these terms vary significantly. Their interchangeable use prevents operationalisations and measures.

OBJECTIVE

This scoping review provides a synthesis of key concepts, definitions and the language used in the context of these terms in an effort to delineate their use for future research.

STUDY SELECTION AND ANALYSIS

Our search on PubMed, EBSCO and Cochrane provided 2835 relevant titles. A total of 176 titles were found eligible for extracting data. A thematic analysis was conducted to synthesise the underlying aspects of individualisation, personalisation and person-centredness. Network visualisations of co-occurring words in 2625 abstracts were performed using VOSViewer.

FINDINGS

Overall, 106 out of 176 (60.2%) articles provided concepts for individualisation, personalisation and person-centredness. Studies using person-centredness provided a conceptualisation more often than the others. A thematic analysis revealed medical, psychological, sociocultural, biological, behavioural, economic and environmental dimensions of the concepts. Practical frameworks were mostly found related to person-centredness, while theoretical frameworks emerged in studies on personalisation. Word co-occurrences showed common psychiatric words in all three network visualisations, but differences in further contexts.

CONCLUSIONS AND CLINICAL IMPLICATIONS

The use of individualisation, personalisation and person-centredness in mental healthcare is multifaceted. While individualisation was the most generic term, personalisation was often used in biomedical or technological studies. Person-centredness emerged as the most well-defined concept, with many frameworks often related to dementia care. We recommend that the use of these terms follows a clear definition within the context of their respective disorders, treatments or medical settings.

SCOPING REVIEW REGISTRATION

Open Science Framework: osf.io/uatsc.

Population Pharmacokinetic Analysis of Perampanel in Portuguese Patients Diagnosed with Refractory Epilepsy

Perampanel is a promising antiepileptic drug (AED) for refractory epilepsy treatment due to its innovative mechanism of action. This study aimed to develop a population pharmacokinetic (PopPK) model to be further used in initial dose optimization of perampanel in patients diagnosed with refractory epilepsy. A total of seventy-two plasma concentrations of perampanel obtained from forty-four patients were analyzed through a population pharmacokinetic approach by means of nonlinear mixed effects modeling (NONMEM). A one-compartment model with first-order elimination best described the pharmacokinetic profiles of perampanel. Interpatient variability (IPV) was entered on clearance (CL), while the residual error (RE) was modeled as proportional. The presence of enzyme-inducing AEDs (EIAEDs) and body mass index (BMI) were found as significant covariates for CL and volume of distribution (V), respectively. The mean (relative standard error) estimates for CL and V of the final model were 0.419 L/h (5.56%) and 29.50 (6.41%), respectively. IPV was 30.84% and the proportional RE was 6.44%. Internal validation demonstrated an acceptable predictive performance of the final model. A reliable population pharmacokinetic model was successfully developed, and it is the first enrolling real-life adults diagnosed with refractory epilepsy.

Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology

More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the computational toxicogenomics as a pivotal part of the next-gen risk assessment paradigm. Artificial Intelligence (AI) has the potential to provid new ways analyzing the patient data and making predictions about treatment outcomes or toxicity. As personalized medicine and toxicogenomics involve huge data processing, AI can expedite this process by providing powerful data processing, analysis, and interpretation algorithms. AI can process and integrate a multitude of data including genome data, patient records, clinical data and identify patterns to derive predictive models anticipating clinical outcomes and assessing the risk of any personalized medicine approaches. In this article, we have studied the current trends and future perspectives in personalized medicine & toxicology, the role of toxicogenomics in connecting the two fields, and the impact of AI on personalized medicine & toxicology. In this work, we also study the key challenges and limitations in personalized medicine, toxicogenomics, and AI in order to fully realize their potential.