Search

Background and aims Precision, personalized or stratified medicine, which promises to deliver the right treatment to the right patient, is a topic of international interest in both the lay press and the scientific literature. A key aspect of precision medicine is the identification of biomarkers that predict the response to medications (i.e. pharmacogenetics). We examined why, despite the great strides that have been made in biomarker identification in many areas of medicine, only in oncology has there been substantial progress in their clinical implementation. We also considered why progress in this effort has lagged in addiction medicine. Methods We compared the development of pharmacogenetic biomarkers in oncology, cardiovascular medicine (where developments are also promising) and addictive disorders. Results The first major reason for the success of oncologic pharmacogenetics is ready access to tumor tissue, which allows in-vitro testing and insights into cancer biology. The second major reason is funding, with cancer research receiving, by far, the largest allocation by the National Institutes of Health (NIH) during the past two decades. The second largest allocation of research funding has gone to cardiovascular disease research. Addictions research received a much smaller NIH funding allocation, despite the major impact that tobacco use, alcohol consumption and illicit drug use have on the public health and healthcare costs. Conclusions Greater support for research on the personalized treatment of addictive disorders can be expected to yield disproportionately large benefits to the public health and substantial reductions in healthcare costs. [ABSTRACT FROM AUTHOR]

The practice of liquid biopsy (LB) has revolutionized the care of patients with metastatic lung cancer. Many oncologists now use this approach in daily practice, applying precise procedures for the detection of activating or resistance mutations in EGFR. These tests are performed with plasma DNA and have been approved as companion diagnostic test for patients treated with tyrosine kinase inhibitors. ALK is another important target in lung cancer since it leads to treatment of patients who are positive for a rearrangement in ALK identified with tumor tissue. By analogy with EGFR, LB for detection of genomic alterations in ALK (rearrangements or mutations) has been rapidly adopted in the clinic. However, this promising approach has some limitations and has not yet been disseminated as much as the blood test targeting EGFR. In addition to these two therapeutic targets LB can be used for evaluation of the genomic status of other genes of interest of patients with lung cancer (ROS1, RET, NTRK MET, BRAF, HER2, etc.). LB can be performed to evaluate a specific target or for a more or less complex panel of genes. Considering the number of potential targets for clinical trials, techniques of next-generation sequencing of circulating DNA are on the rise. This review will provide an update on the contribution of LB to care of patients with metastatic lung cancer, including the present limits of this approach, and will consider certain perspectives. [ABSTRACT FROM AUTHOR]

There is increasing international policy and clinical interest in developing learning health systems and delivering precision medicine, which it is hoped will help reduce variation in the quality and safety of care, improve efficiency, and lead to increasing the personalisation of healthcare. Although reliant on similar policies, informatics tools, and data science and implementation research capabilities, these two major initiatives have thus far largely progressed in parallel. In this opinion piece, we argue that they should be considered as complementary, synergistic initiatives whereby the creation of learning health systems infrastructure can support and catalyse the delivery of precision medicine that maximises the benefits and minimises the risks associated with treatments for individual patients. We illustrate this synergy by considering the example of treatments for asthma, which is now recognised as an umbrella term for a heterogeneous group of related conditions. [ABSTRACT FROM AUTHOR]

Objective: Some eases are thought to be more complex and difficult to treat, although there is little consensus on how to define complexity in psychological care. This study proposes an actuarial, data-driven method of identifying complex cases based on their individual characteristics. Method: Clinical records for 1,512 patients accessing low- and high-intensity psychological treatments were partitioned in 2 random subsamples. Prognostic indices predicting post-treatment reliable and clinically significant improvement (RCSI) in depression (Patient Health Questionnaire-9; Kroenke, Spitzer, & Williams, 2001) and anxiety (Generalized Anxiety Disorder-7; Spitzer, Kroenke. Williams, & Lowe, 2006) symptoms were estimated in 1 subsample using penalized (Lasso) regressions with optimal scaling. A Pi-based algorithm was used to classify patients as standard (St) or complex (Cx) cases in the second (cross-validation) subsample. RCSI rates were compared between Cx cases that accessed treatments of different intensities using logistic regression. Results: St cases had significantly higher RCSI rates compared to Cx cases (OR =1.81 to 2.81). Cx cases tended to attain better depression outcomes if they were initially assigned to high-intensity (vs. low intensity) interventions (OR = 2.23); a similar pattern was observed for anxiety but the odds ratio (1.74) was not statistically significant. Conclusions: Complex cases could be detected early and matched to high-intensity interventions to improve outcomes. [ABSTRACT FROM AUTHOR]

Stratified medicine seeks to identify biomarkers or parsimonious gene signatures distinguishing patients that will benefit most from a targeted treatment. We evaluated 12 approaches in high-dimensional Cox models in randomized clinical trials: penalization of the biomarker main effects and biomarker-by-treatment interactions (full-lasso, three kinds of adaptive lasso, ridge+lasso and group-lasso); dimensionality reduction of the main effect matrix via linear combinations (PCA+lasso (where PCA is principal components analysis) or PLS+lasso (where PLS is partial least squares)); penalization of modified covariates or of the arm-specific biomarker effects (two-I model); gradient boosting; and univariate approach with control of multiple testing. We compared these methods via simulations, evaluating their selection abilities in null and alternative scenarios. We varied the number of biomarkers, of nonnull main effects and true biomarker-by-treatment interactions. We also proposed a novel measure evaluating the interaction strength of the developed gene signatures. In the null scenarios, the group-lasso, two-I model, and gradient boosting performed poorly in the presence of nonnull main effects, and performed well in alternative scenarios with also high interaction strength. The adaptive lasso with grouped weights was too conservative. The modified covariates, PCA+lasso, PLS+lasso, and ridge+lasso performed moderately. The full-lasso and adaptive lassos performed well, with the exception of the full-lasso in the presence of only nonnull main effects. The univariate approach performed poorly in alternative scenarios. We also illustrate the methods using gene expression data from 614 breast cancer patients treated with adjuvant chemotherapy. [ABSTRACT FROM AUTHOR]

Mast cells are tissue-resident cells of the innate immunity, implicated in the pathogenesis of many autoimmune diseases, including rheumatoid arthritis (RA). They are present in synovia and their activation has been linked to the potentiation of inflammation in the course of RA. However, recent investigations questioned the role of mast cells in arthritis. In particular, animal models generated conflicting results, so that many of their pro-inflammatory, i.e. pro-arthritogenic functions, even though supported by robust experimental evidence, have been labelled as redundant. At the same time, a growing body of evidence suggests that mast cells can act as tunable immunomodulatory cells. These characteristics, not yet fully understood in the context of RA, could partially explain the inconsistent results obtained with experimental models, which do not account for the pro- and anti-inflammatory functions exerted in more chronic heterogeneous conditions such as RA. Here we present an overview of the current knowledge on mast cell involvement in RA, including the intriguing hypothesis of mast cells acting as subtle immunomodulatory cells and the emerging concept of synovial mast cells as potential biomarkers for patient stratification. [ABSTRACT FROM AUTHOR]

Background Clinical presentation of nonsteroidal anti-inflammatory drugs exacerbated respiratory disease ( NERD) is found to be heterogeneous. This study classified phenotypic clusters to determine NERD subtypes. Methods We performed two-step cluster analysis using urticaria, chronic rhinosinusitis ( CRS), and atopy, in a NERD cohort comprising 302 patients. Asthma exacerbation was defined as receiving at least one burst of intravenous steroid treatment and/or at least two bursts of oral steroid use (≥ 45 mg/3 days) per year. The possession rate of anti-asthmatic medications was estimated during the follow-up period. Results There were four subtypes: subtype 1 ( NERD with CRS/atopy and no urticaria), subtype 2 ( NERD with CRS and no urticaria/atopy), subtype 3 ( NERD without CRS/urticaria), and subtype 4 ( NERD with urticaria). Significant differences were found between the four subtypes in the female proportion, baseline FEV1%, serum total IgE level, and sputum/peripheral eosinophil count. A higher frequency of asthma exacerbations was noted in subtype 1 compared to subtype 3. The possession rates of medium- to high-dose inhaled corticosteroids/long-acting beta2-agonists showed significant differences among the four subtypes. Metabolomic analysis showed that the four subtypes of NERD had a higher serum leukotriene E4 ( LTE4) level than those with aspirin-tolerant asthma. The patients with subtypes 1 and 3 had a higher urine LTE4 level than those with subtype 2. Conclusion We found four distinct subtypes with different clinical/biochemical findings and asthma exacerbations in a NERD cohort. These findings suggest that stratified strategies by applying subtype classification may help achieve better outcomes in the management of NERD. [ABSTRACT FROM AUTHOR]

Technological advances enabling us to personalise medical interventions at the biological level must be matched by parallel advances in how we support the informed choices essential to patient and public participation. We cannot take participation for granted. To be truly personalised, medicine must take account of the perceptions and capabilities that shape participation. To do this, we need a better understanding of how people perceive personalised medicine and how they judge its value and risks. To realise the promise of 4P medicine we need to personalise at the psychosocial as well as biological dimension, putting the person into personalised medicine. [ABSTRACT FROM AUTHOR]

This article considers why and how self-knowledge is important to communication about risk and behaviour change by arguing for four claims. First, it is doubtful that genetic knowledge should properly be called ‘self-knowledge’ when its ordinary effects on self-motivation and behaviour change seem so slight. Second, temptations towards a reductionist, fatalist, construal of persons’ futures through a ‘molecular optic’ should be resisted. Third, any plausible effort to change people's behaviour must engage with cultural self-knowledge, values and beliefs, catalysed by the communication of genetic risk. For example, while a Judaeo-Christian notion of self-knowledge is distinctively theological, people's self-knowledge is plural in its insight and sources. Fourth, self-knowledge is found in compassionate, if tense, communion which yields freedom from determinism even amidst suffering. Stratified medicine thus offers a newly precise kind of humanising health care through societal solidarity with the riskiest. However, stratification may also mean that molecularly unstratified, ‘B’ patients’ experience involves accentuated suffering and disappointment, a concern requiring further research. [ABSTRACT FROM AUTHOR]

Personalised medicine is widely considered as the way of the future for medicine. However, progress in cancer, with a few outstanding exceptions, has fallen below expectations because of the challenges of tumour heterogeneity and clonal evolution. In both benign and malignant disease, diseases caused by single genetic alterations are more amenable to precision medicine approaches. However, most common diseases are caused by a complex interplay of multiple genetic and environmental factors making personalised medicine far more challenging. The current optimism for personalised medicine is distorting clinical consultations, resource allocation and research funding prioritisation. A research active clinician must act both as an agent of change and development, and as a communicator of realism. Thus personalised medicine that includes a sober appreciation of what genomics can achieve, together with continued focus on the individual as a person not just as a genome, will contribute to further improvements in health and healthcare. [ABSTRACT FROM AUTHOR]

Background Genetic differences in the target proteins, metabolizing enzymes and transporters that contribute to inter-individual differences in drug response are not integrated in contemporary drug development programs. Ayurveda, that has propelled many drug discovery programs albeit for the search of new chemical entities incorporates inter-individual variability “ Prakriti ” in development and administration of drug in an individualized manner. Prakriti of an individual largely determines responsiveness to external environment including drugs as well as susceptibility to diseases. Prakriti has also been shown to have molecular and genomic correlates. We highlight how integration of Prakriti concepts can augment the efficiency of drug discovery and development programs through a unique initiative of Ayurgenomics TRISUTRA consortium. Methods Five aspects that have been carried out are (1) analysis of variability in FDA approved pharmacogenomics genes/SNPs in exomes of 72 healthy individuals including predominant Prakriti types and matched controls from a North Indian Indo-European cohort (2) establishment of a consortium network and development of five genetically homogeneous cohorts from diverse ethnic and geo-climatic background (3) identification of parameters and development of uniform standard protocols for objective assessment of Prakriti types (4) development of protocols for Prakriti evaluation and its application in more than 7500 individuals in the five cohorts (5) Development of data and sample repository and integrative omics pipelines for identification of genomic correlates. Results Highlight of the study are (1) Exome sequencing revealed significant differences between Prakriti types in 28 SNPs of 11 FDA approved genes of pharmacogenomics relevance viz. CYP2C19, CYP2B6, ESR1, F2, PGR, HLA-B, HLA-DQA1, HLA-DRB1, LDLR, CFTR, CPS1. These variations are polymorphic in diverse Indian and world populations included in 1000 genomes project. (2) Based on the phenotypic attributes of Prakriti we identified anthropometry for anatomical features, biophysical parameters for skin types, HRV for autonomic function tests, spirometry for vital capacity and gustometry for taste thresholds as objective parameters. (3) Comparison of Prakriti phenotypes across different ethnic, age and gender groups led to identification of invariant features as well as some that require weighted considerations across the cohorts. Conclusion Considering the molecular and genomics differences underlying Prakriti and relevance in disease pharmacogenomics studies, this novel integrative platform would help in identification of differently susceptible and drug responsive population. Additionally, integrated analysis of phenomic and genomic variations would not only allow identification of clinical and genomic markers of Prakriti for application in personalized medicine but also its integration in drug discovery and development programs. [ABSTRACT FROM AUTHOR]

Background: Stratified Medicine (SM) is becoming a practical reality with the targeting of medicines by using a biomarker or genetic-based diagnostic to identify the eligible patient sub-population. Like any healthcare intervention, SM interventions have costs and consequences that must be considered by reimbursement authorities with limited resources. Methodological standards and guidelines exist for economic evaluations in clinical pharmacology and are an important component for health technology assessments (HTAs) in many countries. However, these guidelines have initially been developed for traditional pharmaceuticals and not for complex interventions with multiple components. This raises the issue as to whether these guidelines are adequate to SM interventions or whether new specific guidance and methodology is needed to avoid inconsistencies and contradictory findings when assessing economic value in SM.Objective: This article describes specific methodological challenges when conducting health economic (HE) evaluations for SM interventions and outlines potential modifications necessary to existing evaluation guidelines /principles that would promote consistent economic evaluations for SM.Results/Conclusions: Specific methodological aspects for SM comprise considerations on the choice of comparator, measuring effectiveness and outcomes, appropriate modelling structure and the scope of sensitivity analyses. Although current HE methodology can be applied for SM, greater complexity requires further methodology development and modifications in the guidelines.

Rapid innovation in (epi)genetics and biomarker sciences is driving a new drug development and product development pathway, with the personalized medicine era dominated by biologic therapeutics and companion diagnostics. Companion diagnostics (CDx) are tests and assays that detect biomarkers and specific mutations to elucidate disease pathways, stratify patient populations, and target drug therapies. CDx can substantially influence the development and regulatory approval for certain high-risk biologics. However, despite the increasingly important role of companion diagnostics in the realization of personalized medicine, in the United States, there are only twenty-three Food and Drug Administration (FDA) approved companion diagnostics on the market for eleven unique indications. Personalized medicines have great potential, yet their use is currently constrained. A major factor for this may lie in the increased complexity of the companion diagnostic and corresponding therapeutic development and adoption pathways. Understanding the market dynamics of companion diagnostic/therapeutic (CDx/Rx) pairs is important to further development and adoption of personalized medicine. Therefore, data collected on a variety of factors may highlight incentives or disincentives driving the development of companion diagnostics. Statistical analysis for thirty-six hypotheses resulted in two significant relationships and thirty-four non-significant relationships. The sensitivity of the companion diagnostic was the only factor that significantly correlated with the price of the companion diagnostic. This result indicates that while there is regulatory pressure for the diagnostic and pharmaceutical industry to collaborate and co-develop companion diagnostics for the approval of personalized therapeutics, there seems to be a lack of parallel economic collaboration to incentivize development of companion diagnostics.

Background: The development of the capability and capacity to evaluate the outcomes of trials of complex interventions is a key priority of the National Institute for Health Research (NIHR) and the Medical Research Council (MRC). The evaluation of complex treatment programmes for mental illness (e.g. cognitive–behavioural therapy for depression or psychosis) not only is a vital component of this research in its own right but also provides a well-established model for the evaluation of complex interventions in other clinical areas. In the context of efficacy and mechanism evaluation (EME) there is a particular need for robust methods for making valid causal inference in explanatory analyses of the mechanisms of treatment-induced change in clinical outcomes in randomised clinical trials. Objectives: The key objective was to produce statistical methods to enable trial investigators to make valid causal inferences about the mechanisms of treatment-induced change in these clinical outcomes. The primary objective of this report is to disseminate this methodology, aiming specifically at trial practitioners. Methods: The three components of the research were (1) the extension of instrumental variable (IV) methods to latent growth curve models and growth mixture models for repeated-measures data; (2) the development of designs and regression methods for parallel trials; and (3) the evaluation of the sensitivity/robustness of findings to the assumptions necessary for model identifiability. We illustrate our methods with applications from psychological and psychosocial intervention trials, keeping the technical details to a minimum, leaving the reporting of the more theoretical and mathematically demanding results for publication in appropriate specialist journals. Results: We show how to estimate treatment effects and introduce methods for EME. We explain the use of IV methods and principal stratification to evaluate the role of putative treatment effect mediators and therapeutic process measures. These results are extended to the analysis of longitudinal data structures. We consider the design of EME trials. We focus on designs to create convincing IVs, bearing in mind assumptions needed to attain model identifiability. A key area of application that has become apparent during this work is the potential role of treatment moderators (predictive markers) in the evaluation of treatment effect mechanisms for personalised therapies (stratified medicine). We consider the role of targeted therapies and multiarm trials and the use of parallel trials to help elucidate the evaluation of mediators working in parallel. Conclusions: In order to demonstrate both efficacy and mechanism, it is necessary to (1) demonstrate a treatment effect on the primary (clinical) outcome, (2) demonstrate a treatment effect on the putative mediator (mechanism) and (3) demonstrate a causal effect from the mediator to the outcome. Appropriate regression models should be applied for (3) or alternative IV procedures, which account for unmeasured confounding, provided that a valid instrument can be identified. Stratified medicine may provide a setting where such instruments can be designed into the trial. This work could be extended by considering improved trial designs, sample size considerations and measurement properties. Funding: The project presents independent research funded under the MRC–NIHR Methodology Research Programme (grant reference G0900678).

Although it is currently recognized that bronchial asthma is not a single disease but a syndrome, we have not yet made use of our new understanding of this heterogeneity as we treat asthma patients. To increase the efficacy of anti-asthma drugs and to decrease costs, it is important to stratify asthma patients into subgroups and to develop therapeutic strategies for each subgroup. Periostin has recently emerged as a biomarker for bronchial asthma, unique in that it is useful not in diagnosis but in categorizing asthma patients. We first found that periostin is a novel component of subepithelial fibrosis in bronchial asthma downstream of IL-13 signals. Thereafter, it was shown that periostin can be a surrogate biomarker of type 2 immune responses, the basis of the notion that a detection system of serum periostin is potentially a companion diagnostic for type 2 antagonists. Furthermore, we have recently shown that serum periostin can predict resistance or hyporesponsiveness to inhaled corticosteroids, based on its contribution to tissue remodeling or fibrosis in bronchial asthma. Thus, serum periostin has two characteristics as a biomarker for bronchial asthma: it is both a surrogate biomarker of type 2 immune responses and a biomarker reflecting tissue remodeling or fibrosis. We can take advantage of these characteristics to develop stratified medicine in bronchial asthma.

Rationale Stratified Medicine (SM) is becoming a natural result of advances in biomedical science and a promising path for the innovation-based biopharmaceutical industry to create new investment opportunities. While the use of biomarkers to improve R&D efficiency and productivity is very much acknowledged by industry, much work remains to be done to understand the drivers and conditions that favour using a stratified approach to create economically viable products and to justify the investment in SM interventions as a stratification option. Concept In this paper we apply a decision analytical methodology to address the economic attractiveness of different SM development options in a cost-contained healthcare environment. For this purpose, a hypothetical business case in the oncology market has been developed considering four feasible development scenarios. Conclusions The article outlines the effects of development time and time to peak sales as key economic value drivers influencing profitability of SM interventions under specific conditions. If regulatory and reimbursement challenges can be solved, decreasing development time and enhancing early market penetration would most directly improve the economic attractiveness of SM interventions. Appropriate tailoring of highly differentiated patient subgroups is the prerequisite to leverage potential efficiency gains in the R&D process. Also, offering a better targeted and hence ultimately more cost-effective therapy at reimbursable prices will facilitate time to market access and allow increasing market share gains within the targeted populations. [ABSTRACT FROM AUTHOR]

A common practice in predictive medicine is to use current study data to construct a stratification procedure, which groups subjects according to baseline information and forms stratum-specific prevention or intervention strategies. A desirable stratification scheme would not only have small intra-stratum variation but also have a clinically meaningful discriminatory capability. We show how to obtain optimal stratification rules with such desirable properties from fitting a set of regression models relating the outcome to baseline covariates and creating scoring systems for predicting potential outcomes. We propose that all available optimal stratifications be evaluated with an independent dataset to select a final stratification. Lastly, we obtain inferential results for this selected stratification scheme with a holdout dataset. When only one study of moderate size is available, we combine the first two steps via crossvalidation. Extensive simulation studies are used to compare the proposed stratification strategy with alternatives. We illustrate the new proposal using an AIDS clinical trial for binary outcomes and a cardiovascular clinical study for censored event time outcomes. [ABSTRACT FROM AUTHOR]

Copyright of Der Onkologe is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics. Objectives: Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case-control, prognostic and longitudinal study designs. Methods: A literature search of the current relevant primary research was performed. Results: Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case-control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance. Conclusion: Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies. [ABSTRACT FROM AUTHOR]

Introduction: There is significant unmet need in Primary Biliary Cholangitis (PBC) in patients under-responsive to the only approved therapy Ursodeoxycholic Acid (UDCA) who are at increased risk of progressing to end-stage liver disease. Obeticholic Acid (OCA) is a farnesoid X receptor (FXR) agonist which has been evaluated as a second line therapy in PBC and has recently been licenced by the FDA. Areascovered: The pharmacology and biology of OCA as an FXR agonist and its clinical benefits. A systematic review was undertaken of published literature, meeting abstracts and trial registries using the search terms FXR, FGF-19 (& FGF-15), Obeticholic Acid and INT-747. Expertcommentary: OCA reduces exposure to toxic hydrophobic bile acids through reduction in bile acid synthesis (by direct and indirect (via enterocyte-released FGF19) actions on Cyp7A1-mediated bile acid synthesis) and bile acid excretion by hepatocytes. It significantly improves liver biochemical parameters strongly associated with risk of disease progression in UDCA under-responsive patients and the key side-effect of pruritus can be reduced by optimised dosing. OCA will be the first stratified therapy introduced in PBC, however confirmatory trial and real life data are needed to confirm that suggestive biochemical improvements are matched by improvement in key clinical outcomes. [ABSTRACT FROM PUBLISHER]

To evaluate a new therapy versus a control via a randomized, comparative clinical study or a series of trials, due to heterogeneity of the study patient population, a pre-specified, predictive enrichment procedure may be implemented to identify an 'enrichable' subpopulation. For patients in this subpopulation, the therapy is expected to have a desirable overall risk-benefit profile. To develop and validate such a 'therapy-diagnostic co-development' strategy, a three-step procedure may be conducted with three independent data sets from a series of similar studies or a single trial. At the first stage, we create various candidate scoring systems based on the baseline information of the patients via, for example, parametric models using the first data set. Each individual score reflects an anticipated average treatment difference for future patients who share similar baseline profiles. A large score indicates that these patients tend to benefit from the new therapy. At the second step, a potentially promising, enrichable subgroup is identified using the totality of evidence from these scoring systems. At the final stage, we validate such a selection via two-sample inference procedures for assessing the treatment effectiveness statistically and clinically with the third data set, the so-called holdout sample. When the study size is not large, one may combine the first two steps using a 'cross-training-evaluation' process. Comprehensive numerical studies are conducted to investigate the operational characteristics of the proposed method. The entire enrichment procedure is illustrated with the data from a cardiovascular trial to evaluate a beta-blocker versus a placebo for treating chronic heart failure patients. [ABSTRACT FROM AUTHOR]

A case-control study from two randomised breast cancer prevention trials of tamoxifen and raloxifene (P-1 and P-2) identified single-nucleotide polymorphisms (SNPs) in or near genes ZNF423 and CTSO as factors which predict which women will derive most anti-cancer benefit from selective oestrogen receptor modulator (SERM) therapy. In this article, we further examine this question using blood samples from two randomised tamoxifen prevention trials: the International Breast Cancer Intervention Study I (IBIS-I) and the Royal Marsden trial (Marsden). A nested case-control study was designed with 2:1 matching in IBIS-I and 1:1 matching in Marsden. The OncoArray was used for genotyping and included two SNPs previously identified (rs8060157 in ZNF423 and rs10030044 near CTSO), and 102 further SNPs within the same regions. Overall, there were 369 cases and 662 controls, with 148 cases and 268 controls from the tamoxifen arms. Odds ratios were estimated by conditional logistic regression, with Wald 95 % confidence intervals. In the tamoxifen arms, the per-allele odds ratio for rs8060157 was 0.99 (95 %CI 0.73-1.34) and 1.00 (95 %CI 0.76-1.33) for rs10030044. In the placebo arm, the odds ratio was 1.10 (95 %CI 0.87-1.40) for rs8060157 and 1.01 (95 %CI 0.79-1.29) for rs10030044. There was no evidence to suggest that other SNPs in the surrounding regions of these SNPs might predict response to tamoxifen. Results from these two prevention trials do not support the earlier findings. rs8060157 in ZNF423 and rs10030044 near CTSO do not appear to predict response to tamoxifen. [ABSTRACT FROM AUTHOR]

Anxiety disorders constitute the largest group of mental disorders with a high individual and societal burden. Neurobiological markers of treatment response bear potential to improve response rates by informing stratified medicine approaches. A systematic review was performed on the current evidence of the predictive value of genetic, neuroimaging and other physiological markers for treatment response (pharmacological and/or psychotherapeutic treatment) in anxiety disorders. Studies published until March 2015 were selected through search in PubMed, Web of Science, PsycINFO, Embase, and CENTRAL. Sixty studies were included, among them 27 on genetic, 17 on neuroimaging and 16 on other markers. Preliminary evidence was found for the functional 5-HTT LPR/rs25531 genotypes, anterior cingulate cortex function and cardiovascular flexibility to modulate treatment outcome. Studies varied considerably in methodological quality. Application of more stringent study methodology, predictions on the individual patient level and cross-validation in independent samples are recommended to set the next stage of biomarker research and to avoid flawed conclusions in the emerging field of “Mental Health Predictomics”. [ABSTRACT FROM AUTHOR]

Background: Stratified Medicine (SM) is becoming a practical reality with the targeting of medicines by using a biomarker or genetic-based diagnostic to identify the eligible patient sub-population. Like any healthcare intervention, SM interventions have costs and consequences that must be considered by reimbursement authorities with limited resources. Methodological standards and guidelines exist for economic evaluations in clinical pharmacology and are an important component for health technology assessments (HTAs) in many countries. However, these guidelines have initially been developed for traditional pharmaceuticals and not for complex interventions with multiple components. This raises the issue as to whether these guidelines are adequate to SM interventions or whether new specific guidance and methodology is needed to avoid inconsistencies and contradictory findings when assessing economic value in SM. Objective: This article describes specific methodological challenges when conducting health economic (HE) evaluations for SM interventions and outlines potential modifications necessary to existing evaluation guidelines /principles that would promote consistent economic evaluations for SM. Results/Conclusions: Specific methodological aspects for SM comprise considerations on the choice of comparator, measuring effectiveness and outcomes, appropriate modeling structure and the scope of sensitivity analyses. Although current HE methodology can be applied for SM, greater complexity requires further methodology development and modifications in the guidelines. [ABSTRACT FROM AUTHOR]

Precision medicine (also called personalized, stratified, or P4 medicine) can be defined as the tailoring of preventive measures and medical treatments to the characteristics of each patient to obtain the best clinical outcome for each person while ideally also enhancing the cost-effectiveness of such interventions for patients and society. Clearly, the best clinical outcome for allergic diseases is not to get them in the first place. To emphasize the importance of disease prevention, a critical component of precision medicine can be referred to as precision health, which is defined herein as the use of all available information pertaining to specific subjects (including family history, individual genetic and other biometric information, and exposures to risk factors for developing or exacerbating disease), as well as features of their environments, to sustain and enhance health and prevent the development of disease. In this article I will provide a personal perspective on how the precision health–precision medicine approach can be applied to the related goals of preventing the development of allergic disorders and providing the most effective diagnosis, disease monitoring, and care for those with these prevalent diseases. I will also mention some of the existing and potential challenges to achieving these ambitious goals. [ABSTRACT FROM AUTHOR]

Objective Previous work has shown that across different patient samples, patients with childhood trauma are more likely to have co-occurrence of affective, anxious and psychosis symptoms than non-traumatized patients. However, the clinical relevance of trauma-related admixture remains to be established. Method We examined patients with mood disorder ( NEMESIS-2; n = 1260), anxiety disorder ( NEMESIS-2; n = 896) or psychotic disorder ( GROUP; n = 532) in terms of symptom profiles, quality of life ( QOL) and social functioning. Results Results showed that mood disorder patients with both trauma and co-occurrence of affective, anxious and psychosis symptoms had a lower QOL ( B-12.6, 95% CI −17.7 to −7.5, P < 0.001), more help-seeking behaviour [odds ratio ( OR) 2.5, 95% CI 1.1-5.7, P = 0.031] and higher prevalence of substance use disorders ( OR 7.8, 95% CI 1.1-58.0, P = 0.044), compared with patients without trauma history and symptom admixture (Trauma−/ CL−). Similar results were found in patients with an anxiety disorder. Traumatized patients with a psychotic disorder and admixture showed lower QOL ( B-0.6, 95% CI −0.9 to −0.4, P < 0.001), higher prevalence of drug disorders ( OR 2.2, 95% CI 1.2-3.9, P = 0.008) and lower global assessment of functioning ( B-12.8, 95% CI −17.1 to −8.5, P < 0.001) than Trauma−/ CL− patients. Conclusion Stratification according to childhood trauma exposure thus identifies a phenotype characterized by admixture of affective, anxiety and psychotic symptoms that, when combined, has clinical relevance. Identification of functionally meaningful aetiological subgroups may aid clinical practice. [ABSTRACT FROM AUTHOR]

Background and Aims: Nephrotoxicity is a rare idiosyncratic reaction to 5-aminosalicylate (5-ASA) therapies. The aims of this study were to describe the clinical features of this complication and identify clinically useful genetic markers so that these drugs can be avoided or so that monitoring can be intensified in high-risk patients. Methods: Inflammatory bowel disease patients were recruited from 89 sites around the world. Inclusion criteria included normal renal function prior to commencing 5-ASA, >50% rise in creatinine any time after starting 5-ASA, and physician opinion implicating 5-ASA strong enough to justify drug withdrawal. An adjudication panel identified definite and probable cases from structured case report forms. A genome-wide association study was then undertaken with these cases and 4109 disease controls. Results: After adjudication, 151 cases of 5-ASA-induced nephrotoxicity were identified. Sixty- eight percent of cases were males, with nephrotoxicity occurring at a median age of 39.4 years (range 6-79 years). The median time for development of renal injury after commencing 5-ASA was 3.0 years (95% confidence interval [CI] 2.3-3.7). Only 30% of cases recovered completely after drug withdrawal, with 15 patients requiring permanent renal replacement therapy. A genome-wide association study identified a suggestive association in the HLA region (p = 1 x 10-7) with 5-ASA- induced nephrotoxicity. A sub-group analysis of patients who had a renal biopsy demonstrating interstitial nephritis (n = 55) significantly strengthened this association (p = 4 x 10-9, odds ratio 3.1). Conclusions: This is the largest and most detailed study of 5-ASA-induced nephrotoxicity to date. It highlights the morbidity associated with this condition and identifies for the first time a significant genetic predisposition to drug-induced renal injury. [ABSTRACT FROM AUTHOR]

Background: 3% of kidney transplant recipients return to dialysis annually upon allograft failure. Development of antibodies (Ab) against human leukocyte antigens (HLA) is a validated prognostic biomarker of allograft failure. We tested whether screening for HLA Ab, combined with an intervention to improve adherence and optimization of immunosuppression could prevent allograft failure., Methods: Prospective, open-labelled randomised biomarker-based strategy (hybrid) trial in 13 UK transplant centres [EudraCT (2012-004308-36) and ISRCTN (46157828)]. Patients were randomly allocated (1:1) to unblinded or double-blinded arms and screened every 8 months. Unblinded HLA Ab+ patients were interviewed to encourage medication adherence and had tailored optimisation of Tacrolimus, Mycophenolate mofetil and Prednisolone. The primary outcome was time to graft failure in an intention to treat analysis. The trial had 80% power to detect a hazard ratio of 0.49 in donor specific antibody (DSA)+ patients., Findings: From 11/9/13 to 27/10/16, 5519 were screened for eligibility and 2037 randomised (1028 to unblinded care and 1009 to double blinded care). We identified 198 with DSA and 818 with non-DSA. Development of DSA, but not non-DSA was predictive of graft failure. HRs for graft failure in unblinded DSA+ and non-DSA+ groups were 1.54 (95% CI: 0.72 to 3.30) and 0.97 (0.54-1.74) respectively, providing no evidence of an intervention effect. Non-inferiority for the overall unblinded versus blinded comparison was not demonstrated as the upper confidence limit of the HR for graft failure exceeded 1.4 (1.02, 95% CI: 0.72 to 1.44). The only secondary endpoint reduced in the unblinded arm was biopsy-proven rejection., Interpretation: Intervention to improve adherence and optimize immunosuppression does not delay failure of renal transplants after development of DSA. Whilst DSA predicts increased risk of allograft failure, novel interventions are needed before screening can be used to direct therapy., Funding: The National Institute for Health Research Efficacy and Mechanism Evaluation programme grant (ref 11/100/34)., Competing Interests: DB declares consulting fees and speaker honoraria from Hansa Biopharma. RT declares membership of ESOT Education Committee (2018–2021) (expenses reimbursed). PM declares research funding from NIHR. AD declares research funding from the 10.13039/501100007155Medical Research Council, consulting fees (paid to KCL) from Hansa Biopharma, Verici Diagnostics, UCB Pharma and Quell Therapeutics, Membership of the Herperis Faculty 2019, 2021 and 2022 (expenses reimbursed), Membership of the UK Organ donation and transplantation research network executive since 2020 (unpaid), Membership of the EME funding Committee (2014–2019) and the EME funding committee subgroup (2018–2019) (both unpaid). The remaining authors declare no conflicts of interest., (© 2023 The Authors.)

The drivers of precision medicine are clear: for patients (and physicians) - more options, durable clinical benefit, reduced exposure to non-effective drugs and potential to leverage current scientific and technological advances; for the pharmaceutical industry - the potential to tackle core challenges in discovering and developing better and more efficacious medicines, to reduce rates of attrition in drug development and to reduce development costs; for healthcare systems and payers - improved efficiency through the provision of effective care and avoiding ineffective treatments. Oncology has been at the vanguard, the improvements gained in patient survival notable. However, the increasing number of molecular subgroups requires an equally increasing number (and new generation) of highly selective agents targeting inevitably lower incidence molecular segments. Innovative trial designs (umbrella/basket studies) are emerging as a patient-centric approach to drug development, and the rise in public-private partnerships, cross-industry, government and non-profit sector collaborations is enabling implementation of complex clinical trial designs. This poses significant challenges for healthcare systems and regulatory approval. Further substantial evolution of policy and processes, particularly regulatory requirements for approval for new therapeutics, are required. © 2015 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Background: The management of NSCLC has been transformed by stratified medicine. The National Lung Matrix Trial (NLMT) is a UK-wide study exploring the activity of rationally selected biomarker/targeted therapy combinations. Patients and methods: The Cancer Research UK (CRUK) Stratified Medicine Programme 2 is undertaking the large volume national molecular pre-screening which integrates with the NLMT. At study initiation, there are eight drugs being used to target 18 molecular cohorts. The aim is to determine whether there is sufficient signal of activity in any drug--biomarker combination to warrant further investigation. A Bayesian adaptive design that gives a more realistic approach to decision making and flexibility to make conclusions without fixing the sample size was chosen. The screening platform is an adaptable 28-gene Nextera next-generation sequencing platform designed by Illumina, covering the range of molecular abnormalities being targeted. The adaptive design allows new biomarker-drug combination cohorts to be incorporated by substantial amendment. The pre-clinical justification for each biomarker--drug combination has been rigorously assessed creating molecular exclusion rules and a trumping strategy in patients harbouring concomitant actionable genetic abnormalities. Discrete routes of pathway activation or inactivation determined by cancer genome aberrations are treated as separate cohorts. Key translational analyses include the deep genomic analysis of pre- and post-treatment biopsies, the establishment of patient-derived xenograft models and longitudinal ctDNA collection, in order to define predictive biomarkers, mechanisms of resistance and early markers of response and relapse. Conclusion: The SMP2 platform will provide large scale genetic screening to inform entry into the NLMT, a trial explicitly aimed at discovering novel actionable cohorts in NSCLC. Clinical Trial ISRCTN: 38344105. [ABSTRACT FROM AUTHOR]

The idea that different patients will respond differently to the same treatment is not new. The recent advances in genomics and laboratory medicine have led to the hope that it will be possible to maximize the benefit and minimize the harms of each medical therapy by using an individuals' biomarker status to 'personalize' their treatment. The selection of treatment for each individual would then be determined, not just by their disease status (or an estimate of the risk of developing a disease or disease progression), but also by their genetic makeup or by other measurable characteristics, such as the level of a particular biomarker in the blood. This review discusses the extent to which personalized medicine might be applied in stroke, and the implications for global stroke health care. [ABSTRACT FROM AUTHOR]