Subcutaneous administration of natalizumab can lead to lower drug concentrations compared to intravenous administration.

BACKGROUND: Several studies reported lower drug concentrations with subcutaneous natalizumab compared to intravenous natalizumab. With the emergence of extended interval dosing, gaining more insight into lower concentrations after subcutaneous administration is essential. METHODS: We compared serum trough concentrations between subcutaneous and intravenous administration within a matched cohort (n = 50). RESULTS: Subcutaneous administration (n = 25) was associated with lower concentrations compared to intravenous administration (n = 25) (log-B=-0.28, p = 0.01). In an exploratory group of 11 patients receiving extended interval dosing of subcutaneous natalizumab, the median trough concentration was even lower. CONCLUSION: Subcutaneous natalizumab can lead to lower drug concentrations, potentially limiting extended interval dosing.

Personalised music as a treatment for epilepsy.

In this paper we look at non-pharmaceutical treatments for intractable epilepsy based on neurophysiological methods especially with EEG analysis. In summary, there are a number of limbic and thalamo-cortical related structures involved in the processing of musical emotion (exposure), including the amygdala (arousal, expression of mood, fear), hippocampus (memory, regulation of HPA axis, stress), parahippocampal gyrus (recognition, memory retrieval), insula (valence), temporal poles (connectivity), ventral striatum (expectation and experience of reward), orbitofrontal cortex (valence) and cingulate cortex (autonomic regulation). One method is to audify (a form of sonification) EEG activity to find music by feedback to entrain abnormal EEG activity. We discuss various methods and our use of X-System (https://www.x-system.co.uk/) which is a computational model of the musical brain capable of predicting the neurophysiological effects of music. It models structures and pathways related to responses to music, including the cochlea, brain stem, auditory and motor cortex, as well as basal ganglia, cerebellum and limbic structures. It can predict autonomic and endocrine activity as well as the substrates of electrical activity to select music which can regularise EEG abnormalities to decrease epileptic activity and seizures, especially in those unresponsive to antiepileptic medication or invasive treatments.

Predicting post-treatment symptom severity for adults receiving psychological therapy in routine care for generalised anxiety disorder: a machine learning approach.

Approximately half of generalised anxiety disorder (GAD) patients do not recover from first-line treatments, and no validated prediction models exist to inform individuals or clinicians of potential treatment benefits. This study aimed to develop and validate an accurate and explainable prediction model of post-treatment GAD symptom severity. Data from adults receiving treatment for GAD in eight Improving Access to Psychological Therapies (IAPT) services (n=15,859) were separated into training, validation and holdout datasets. Thirteen machine learning algorithms were compared using 10-fold cross-validation, against two simple clinically relevant comparison models. The best-performing model was tested on the holdout dataset and model-specific explainability measures identified the most important predictors. A Bayesian Additive Regression Trees model out-performed all comparison models (MSE=16.54 [95 % CI=15.58; 17.51]; MAE=3.19; R²=0.33, including a single predictor linear regression model: MSE=20.70 [95 % CI=19.58; 21.82]; MAE=3.94; R²=0.14). The five most important predictors were: PHQ-9 anhedonia, GAD-7 annoyance/irritability, restlessness and fear items, then the referral-assessment waiting time. The best-performing model accurately predicted post-treatment GAD symptom severity using only pre-treatment data, outperforming comparison models that approximated clinical judgement and remaining within the GAD-7 error of measurement and minimal clinically important differences. This model could inform treatment decision-making and provide desired information to clinicians and patients receiving treatment for GAD.

SF-CORNER (splenic flexure colorectal cancer): an international survey of operative approaches and outcomes for cancers of the splenic flexure.

AIM: The optimum surgical approach to splenic flexure cancers (SFCs) remains uncertain. The aim of this survey was to explore the opinions of an international surgical community on the management and outcomes of SFC. METHOD: A questionnaire was constructed comprising five sections (information about respondents; definition and prognosis of SFC; operative approach; approach in specific scenarios; outcomes) and circulated through an international dissemination committee and social media. RESULTS: The survey received 576 responses over 4 weeks across 50 countries. There was no consensus regarding the definition of the splenic flexure, whilst the proportion of respondents who did and did not think that patients with SFC had a worse outcome was equal. The overall preferred operative approach was left hemicolectomy [203 (35.2%)], followed by segmental resection [167 (29%)], extended right hemicolectomy [126 (21.9%)] and subtotal colectomy [7 (12%)]. The stated pedicles for ligation varied between resection types and also within the same resection. One hundred and sixty-six (28.8%) respondents thought a segmental resection was associated with the worst survival and 190 (33%) thought it was associated with the best quality of life. CONCLUSION: This survey confirms a lack of consensus across all aspects SFC treatment. The differing approaches described are likely to represent different beliefs around the variable anatomy of this region and the associated lymphatic drainage. Future studies are required to address such inconsistencies and identify the optimum surgical strategy, whilst also incorporating quality-of-life metrics and patient-reported outcomes. A one-size-fits-all approach is probably not appropriate with SFC, and a more bespoke approach is required.

Dkk1 as a Prognostic Marker for Neoadjuvant Chemotherapy Response in Breast Cancer Patients.

PURPOSE: To investigate the role of Dkk1 as a predictor of response to NACT in BC patients. METHODS: This retrospective monocentric study included 145 women who had undergone NACT followed by breast surgery. Dkk1 protein expression was assessed using immunohistochemistry staining in core needle biopsies and mammary carcinoma specimens. RESULTS: Dkk1 levels were lower in treated BC tumours than in untreated tumours. The outcomes of 68 matched pre- and post-therapy tissues showed that Dkk1 levels in mammary carcinoma tissues were significantly predicted by levels in core needle biopsies and that Dkk1 expression was reduced in 83% of cases. Smaller cT stage, positive Her2 expression, and decreased Dkk1-IRS in core needle biopsy tissues were all independent predictors of regression grade (R4), according to Sinn. However, the percentage of Dkk1 expression differences prior to and following NACT had no effect on PFS or OS. CONCLUSIONS: In this study, we demonstrated for the first time that Dkk1 could be identified as an independent predictor of NACT response in BC patients, particularly those with TNBC. Further research with a multicentric expanded (pre-/post-therapy) sample set and better-defined populations in terms of molecular subtypes, therapy modality, and long-term follow-up is recommended to obtain more solid evidence.

UGT1A1 genotype-guided irinotecan dosing during neoadjuvant chemoradiotherapy for locally advanced rectal cancer: A prospective analysis of SN-38 concentration.

Irinotecan plays a crucial role in the neoadjuvant chemoradiotherapy (nCRT) of rectal cancer, but its optimal dosing is still unclear. In this study, we included 101 eligible patients with the UGT1A1*28 genotype of UGT1A1*1*1 (74.3%) and UGT1A1*1*28 (25.7%) and UGT1A1*6 genotypes of GG (63.4%), GA (32.7%), and AA (3.9%). All patients received preoperative radiotherapy (50 Gy/25 fractions) with concurrent irinotecan (UGT1A1*1*1: 80 mg/m2 ; UGT1A1*1*28: 65 mg/m2 ) and capecitabine (CapIri). SN-38 concentrations were measured at 1.5, 24, and 49 h post-administration. Patients were divided into four groups (Q1-Q4) based on the SN-38 concentration. The complete-response (CR) rate was the primary endpoint. The analysis demonstrated that the 49 h SN-38 concentration was relatively optimal for predicting efficacy and toxicity. The Q4 group had a significantly higher CR rate than the Q1 group (p = .019), but also higher rates of adverse events (p = .009). We screened the recommended 49 h SN-38, with a 0.5-1.0 ng/mL concentration range. We also validated the correlation between UGT1A1*6 polymorphism and SN-38 concentration, along with the clinical efficacy of irinotecan. In conclusion, our study identified the relatively optimal timepoint and concentration range for monitoring SN38 concentrations and revealed the clinical significance of UGT1A1*6 and UGT1A1*28 polymorphisms in guiding irinotecan administration, offering meaningful insights for personalised irinotecan dosing.

The Evolution of Affordable Technologies in Liquid Biopsy Diagnostics: The Key to Clinical Implementation.

Cancer remains a leading cause of death worldwide, despite many advances in diagnosis and treatment. Precision medicine has been a key area of focus, with research providing insights and progress in helping to lower cancer mortality through better patient stratification for therapies and more precise diagnostic techniques. However, unequal access to cancer care is still a global concern, with many patients having limited access to diagnostic tests and treatment regimens. Noninvasive liquid biopsy (LB) technology can determine tumour-specific molecular alterations in peripheral samples. This allows clinicians to infer knowledge at a DNA or cellular level, which can be used to screen individuals with high cancer risk, personalize treatments, monitor treatment response, and detect metastasis early. As scientific understanding of cancer pathology increases, LB technologies that utilize circulating tumour DNA (ctDNA) and circulating tumour cells (CTCs) have evolved over the course of research. These technologies incorporate tumour-specific markers into molecular testing platforms. For clinical translation and maximum patient benefit at a wider scale, the accuracy, accessibility, and affordability of LB tests need to be prioritized and compared with gold standard methodologies in current use. In this review, we highlight the range of technologies in LB diagnostics and discuss the future prospects of LB through the anticipated evolution of current technologies and the integration of emerging and novel ones. This could potentially allow a more cost-effective model of cancer care to be widely adopted.

Advancements in Biotechnology and Stem Cell Therapies for Breast Cancer Patients.

This comprehensive review article examines the integration of biotechnology and stem cell therapy in breast cancer diagnosis and treatment. It discusses the use of biotechnological tools such as liquid biopsies, genomic profiling, and imaging technologies for accurate diagnosis and monitoring of treatment response. Stem cell-based approaches, their role in modeling breast cancer progression, and their potential for breast reconstruction post-mastectomy are explored. The review highlights the importance of personalized treatment strategies that combine biotechnological tools and stem cell therapies. Ethical considerations, challenges in clinical translation, and regulatory frameworks are also addressed. The article concludes by emphasizing the potential of integrating biotechnology and stem cell therapy to improve breast cancer outcomes, highlighting the need for continued research and collaboration in this field.

Development of Organs-on-Chips and Their Impact on Precision Medicine and Advanced System Simulation.

Drugs may undergo costly preclinical studies but still fail to demonstrate their efficacy in clinical trials, which makes it challenging to discover new drugs. Both in vitro and in vivo models are essential for disease research and therapeutic development. However, these models cannot simulate the physiological and pathological environment in the human body, resulting in limited drug detection and inaccurate disease modelling, failing to provide valid guidance for clinical application. Organs-on-chips (OCs) are devices that serve as a micro-physiological system or a tissue-on-a-chip; they provide accurate insights into certain functions and the pathophysiology of organs to precisely predict the safety and efficiency of drugs in the body. OCs are faster, more economical, and more precise. Thus, they are projected to become a crucial addition to, and a long-term replacement for, traditional preclinical cell cultures, animal studies, and even human clinical trials. This paper first outlines the nature of OCs and their significance, and then details their manufacturing-related materials and methodology. It also discusses applications of OCs in drug screening and disease modelling and treatment, and presents the future perspective of OCs.

3D printing of LEGO® like designs with tailored release profiles for treatment of sleep disorder.

3D printed LEGO®-like designs are an attractive approach for the development of compartmental delivery systems due to their potential for dose personalisation through the customisation of drug release profiles. Additive manufacturing technologies such as Fused Deposition Modelling (FDM) are ideal for the printing of structures with complex geometries and various sizes. This study is a paradigm for the fabrication of 3D printed LEGO® -like tablets by altering the design of the modular units and the filament composition for the delivery of different drug substances. By using a combination of placebo and drug loaded compartments comprising of immediate release (hydroxypropyl cellulose) and pH dependant polymers (hypromellose acetate succinate) we were able to customise the release kinetics of melatonin and caffeine that can potentially be used for the treatment of sleep disorders. The LEGO® -like compartments were designed to achieve immediate release of melatonin followed by variable lag times and controlled release of caffeine.

Serum N-Glycomic Biomarkers Predict Treatment Escalation in Inflammatory Bowel Disease.

Biomarkers to guide clinical decision making at diagnosis of inflammatory bowel disease [IBD] are urgently needed. We investigated a composite serum N-glycomic biomarker to predict future disease course in a discovery cohort of 244 newly diagnosed IBD patients. In all, 47 individual glycan peaks were analysed using ultra-high performance liquid chromatography, identifying 105 glycoforms from which 24 derived glycan traits were calculated. Multivariable logistic regression was performed to determine associations of derived glycan traits with disease. Cox proportional hazard models were used to predict treatment escalation from first-line treatment to biologics or surgery (hazard ratio [HR] 25.9, p = 1.1 × 10-12; 95% confidence interval [CI], 8.52-78.78). Application to an independent replication cohort of 54 IBD patients yielded an HR of 5.1 [p = 1.1 × 10-5; 95% CI, 2.54-10.1]. These data demonstrate the prognostic capacity of serum N-glycan biomarkers and represent a step towards personalised medicine in IBD.

Digital twin in healthcare: Recent updates and challenges.

As simulation is playing an increasingly important role in medicine, providing the individual patient with a customised diagnosis and treatment is envisaged as part of future precision medicine. Such customisation will become possible through the emergence of digital twin (DT) technology. The objective of this article is to review the progress of prominent research on DT technology in medicine and discuss the potential applications and future opportunities as well as several challenges remaining in digital healthcare. A review of the literature was conducted using PubMed, Web of Science, Google Scholar, Scopus and related bibliographic resources, in which the following terms and their derivatives were considered during the search: DT, medicine and digital health virtual healthcare. Finally, analyses of the literature yielded 465 pertinent articles, of which we selected 22 for detailed review. We summarised the application examples of DT in medicine and analysed the applications in many fields of medicine. It revealed encouraging results that DT is being increasing applied in medicine. Results from this literature review indicated that DT healthcare, as a key fusion approach of future medicine, will bring the advantages of precision diagnose and personalised treatment into reality.

A Multicentre Clinical Study of Sarcoma Personalised Treatment Using Patient-Derived Tumour Xenografts.

AIMS: Medication for advanced sarcomas has not improved for three decades. Patient-derived tumour xenografts (PDTX) are a promising solution for developing new therapies and real-time personalised medicine because of their highly effective prediction of drug efficacy. However, there is a dearth of PDTX models for sarcomas due to the scarcity and heterogeneity of the disease. MATERIALS AND METHODS: A multicentre clinical collaborative study (ChiCTR-OOC-17013617) was carried out. Fresh patient tumour tissues via resection or biopsy were used for the PDTX set-up. The standard medical care chosen by the physician was given to the patient, in parallel with testing on multiple regimens. The outcomes of patients' responses and PDTX tests were compared. Comprehensive analyses were carried out to assess the clinical value of PDTX for the treatment of sarcomas. Living tissues from successfully engrafted cases were deposited into a repository. RESULTS: Forty-two cases, including 36 bone sarcomas and six soft-tissue sarcomas, were enrolled; the overall engraftment rate was 73.8%. Histopathological examination showed a 100% consistency between primary tumours and tumour grafts. The engraftment rate was independent of age, gender and sampling methods, but was associated with subtypes of tumour. The outgrowth time of tumour grafts could be associated with prognosis. Major somatic mutations in tumour grafts occurred primarily in common tumour driver genes. Poor prognosis was associated with the KMT2C mutation. A drug efficacy test showed complete concordance between the PDTX model and patients' responses in 17 regimens. CONCLUSION: PDTX is an ideal preclinical model for sarcomas because of its faithful preservation of the heterogeneity of the disease, a satisfactory engraftment rate and high accuracy in its prediction of drug efficacy.

Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model.

BACKGROUND: Donepezil, galantamine, rivastigmine and memantine are potentially effective interventions for cognitive impairment in dementia, but the use of these drugs has not been personalised to individual patients yet. We examined whether artificial intelligence-based recommendations can identify the best treatment using routinely collected patient-level information. METHODS: Six thousand eight hundred four patients aged 59-102 years with a diagnosis of dementia from two National Health Service (NHS) Foundation Trusts in the UK were used for model training/internal validation and external validation, respectively. A personalised prescription model based on the Recurrent Neural Network machine learning architecture was developed to predict the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores post-drug initiation. The drug that resulted in the smallest decline in cognitive scores between prescription and the next visit was selected as the treatment of choice. Change of cognitive scores up to 2 years after treatment initiation was compared for model evaluation. RESULTS: Overall, 1343 patients with MMSE scores were identified for internal validation and 285 [21.22%] took the drug recommended. After 2 years, the reduction of mean [standard deviation] MMSE score in this group was significantly smaller than the remaining 1058 [78.78%] patients (0.60 [0.26] vs 2.80 [0.28]; P = 0.02). In the external validation cohort (N = 1772), 222 [12.53%] patients took the drug recommended and reported a smaller MMSE reduction compared to the 1550 [87.47%] patients who did not (1.01 [0.49] vs 4.23 [0.60]; P = 0.01). A similar performance gap was seen when testing the model on patients prescribed with AChEIs only. CONCLUSIONS: It was possible to identify the most effective drug for the real-world treatment of cognitive impairment in dementia at an individual patient level. Routine care patients whose prescribed medications were the best fit according to the model had better cognitive performance after 2 years.

Gene expression profiling in melanoma: A view from the clinic.

The treatment of Melanoma, one of the most aggressive human malignancies, has been revolutionised by the advent of novel targeted and immuno-therapies. However, methods utilised to detect early presentations, and to stratify risk for patients diagnosed with invasive melanoma in the clinical setting are lagging. The primary prognostic indicator is restricted to Breslow Thickness, or depth the tumour invades into the dermis. Gene Expression Profiling (GEP), the analysis of molecular gene signatures of an individual tumour, has been implemented with great success in other malignancies, such as breast and prostate cancer. In the setting of melanoma, commercial GEP panels are becoming available, offering patients a personalised approach, though yet to enter widespread clinical use. This short perspective seeks to describe how GEP is currently employed in practice, and its current clinical impact. We also suggest the potential roles for GEP in meeting the key clinical challenges faced by clinicians in melanoma treatment, such as decisions around adjuvant therapy, sentinel lymph node biopsy (SLNB) and surgical resection , thus highlighting areas for future potential research.

Challenges Involved in Incorporating Personalised Treatment Plan as Routine Care of Patients with Diabetes.

Diabetes is a heterogenous disorder, and patients with this disorder vary considerably in their clinical presentation, response to therapy and risk of complications. Expanding knowledge about the pathophysiology of various forms of diabetes has raised the possibility that diagnostic and therapeutic modalities can be tailored to the individual patient in a personalized manner. The recent publication of a Consensus Statement on precision diabetes care underlines the major strides made in this field in the recent past. However, while personalized diabetes care has the potential to significantly improve outcomes in patients with diabetes in a safe and cost-effective manner, its wider application presents several challenges, especially in resource-strained settings. These challenges pertain equally to precision diagnostics, precision therapeutics and precision monitoring. This article discusses some of the important challenges that care providers are likely to face in applying the personalized approach in caring for their patients with diabetes, in the context of diagnosis and management of type 1 diabetes, type 2 diabetes and monogenic forms of diabetes. Suggestions are also presented for overcoming some of these challenges.

A comparison of approaches for combining predictive markers for personalised treatment recommendations.

BACKGROUND: In the presence of heterogeneous treatment effects, it is desirable to divide patients into subgroups based on their expected response to treatment. This is formalised via a personalised treatment recommendation: an algorithm that uses biomarker measurements to select treatments. It could be that multiple, rather than single, biomarkers better predict these subgroups. However, finding the optimal combination of multiple biomarkers can be a difficult prediction problem. METHODS: We described three parametric methods for finding the optimal combination of biomarkers in a personalised treatment recommendation, using randomised trial data: a regression approach that models outcome using treatment by biomarker interactions; an approach proposed by Kraemer that forms a combined measure from individual biomarker weights, calculated on all treated and control pairs; and a novel modification of Kraemer's approach that utilises a prognostic score to sample matched treated and control subjects. Using Monte Carlo simulations under multiple data-generating models, we compare these approaches and draw conclusions based on a measure of improvement under a personalised treatment recommendation compared to a standard treatment. The three methods are applied to data from a randomised trial of home-delivered pragmatic rehabilitation versus treatment as usual for patients with chronic fatigue syndrome (the FINE trial). Prior analysis of this data indicated some treatment effect heterogeneity from multiple, correlated biomarkers. RESULTS: The regression approach outperformed Kraemer's approach across all data-generating scenarios. The modification of Kraemer's approach leads to improved treatment recommendations, except in the case where there was a strong unobserved prognostic biomarker. In the FINE example, the regression method indicated a weak improvement under its personalised treatment recommendation algorithm. CONCLUSIONS: The method proposed by Kraemer does not perform better than a regression approach for combining multiple biomarkers. All methods are sensitive to misspecification of the parametric models.

Exploring the role of age as a moderator of cognitive remediation for people with schizophrenia.

BACKGROUND: While Cognitive Remediation (CR) is effective in reducing cognitive and functioning difficulties in people with schizophrenia, there is variability in treatment response. Previous research suggested that participants' age may be a significant moderator of CR response. AIM: To examine the impact of participants' age on CR outcomes. METHOD: Individual participant data were accessed from fourteen CR randomised controlled trials. We tested the moderating effect of participants' age on cognitive and functioning outcomes using multivariate linear models. RESULTS: Data from 1084 people with a diagnosis of schizophrenia were considered. Participants had a mean age of 36.6 years (SD 11), with 11.6 years of education (SD 2.8), and an average duration of illness of 13.5 years (SD 10.7). Multivariate models showed that participants' age, when considered as a continuous variable, was not a significant moderator of treatment effect for cognitive and functioning outcomes. However, when participants were split by median age, younger participants showed higher gains in executive functions following CR compared to older participants (p=0.02). CONCLUSION: These results suggest that participants' age does not moderate most CR outcomes. However, larger age differences may influence the effect of CR on executive function. This may suggest some adaptation of CR practice according to participants' age. These findings inform the CR personalisation agenda.

Possible Interventions for Preventing the Development of Psychopathic Traits among Children and Adolescents?

Individuals with the combination of psychopathy and severe conduct disorder often get in a lot of trouble from their early childhood, and can cause great suffering and problems for other people and their immediate environment. Their antisocial behaviour has a tendency to develop into a chronic pattern early in life, and the treatment prognosis in adulthood is poor. A large proportion of serious violent crimes in society can be attributed to this group of perpetrators. Until recently, it has been unclear whether traits of this type can be prevented or changed, so that these individuals and their surroundings can benefit from targeted treatments at an early stage. To reduce serious crime in a society, it is very important to develop effective measures for this particular group. A lack of empathy, indifference to others, and a lack of concern about their own performance appear to be key early signs in children and adolescents with persistent behavioural problems and more serious norm violations who continue into a criminal career upon reaching adulthood. These characteristics have been termed callous-unemotional (CU) traits, and they are considered to be a precursor to psychopathic traits in adulthood. In recent years, several studies have evaluated the degree to which treatments that have been proved effective for children and adolescents with severe behavioural problems also show effectiveness for children and adolescents with CU traits. Interventions specifically tailored to children with CU traits have also been developed with the aim of directly changing the ongoing development of this precursor to psychopathy. In this paper, we will address the extent to which current evidence-based treatment methods developed for children and adolescents with behavioural difficulties are equally effective when a child has CU traits. We will also take a closer look at the effects of interventions designed to change this trait. There will be a discussion regarding what seems relevant for a change in the trait itself, as well as a change in their antisocial behaviour.

Imaging Biomarkers of Tumour Proliferation and Invasion for Personalised Lung Cancer Therapy.

Personalised treatment in oncology has seen great developments over the last decade, due to both technological advances and more in-depth knowledge of radiobiological processes occurring in tumours. Lung cancer therapy is no exception, as new molecular targets have been identified to further increase treatment specificity and sensitivity. Yet, tumour resistance to treatment is still one of the main reasons for treatment failure. This is due to a number of factors, among which tumour proliferation, the presence of cancer stem cells and the metastatic potential of the primary tumour are key features that require better controlling to further improve cancer management in general, and lung cancer treatment in particular. Imaging biomarkers play a key role in the identification of biological particularities within tumours and therefore are an important component of treatment personalisation in radiotherapy. Imaging techniques such as PET, SPECT, MRI that employ tumour-specific biomarkers already play a critical role in patient stratification towards individualized treatment. The aim of the current paper is to describe the radiobiological challenges of lung cancer treatment in relation to the latest imaging biomarkers that can aid in the identification of hostile cellular features for further treatment adaptation and tailoring to the individual patient's needs.