Prognostic iron-metabolism signature robustly stratifies single-cell characteristics of hepatocellular carcinoma

Cancer immunotherapy has shown to be a promising method in treating hepatocellular carcinoma (HCC), but suboptimal responses in patients are attributed to cellular and molecular heterogeneity. Iron metabolism-related genes (IRGs) are important in maintaining immune system homeostasis and have the potential to help develop new strategies for HCC treatment. Herein, we constructed and validated the iron-metabolism gene prognostic index (IPX) using univariate Cox proportional hazards regression and LASSO Cox regression analysis, successfully categorizing HCC patients into two groups with distinct survival risks. Then, we performed single-sample gene set enrichment analysis, weighted correlation network analysis, gene ontology enrichment analysis, cellular lineage analysis, and SCENIC analysis to reveal the key determinants underlying the ability of this model based on bulk and single-cell transcriptomic data. We identified several driver transcription factors specifically activated in specific malignant cell sub-populations to contribute to the adverse survival outcomes in the IPX-high subgroup. Within the tumor microenvironment (TME), T cells displayed significant diversity in their cellular characteristics and experienced changes in their developmental paths within distinct clusters identified by IPX. Interestingly, the proportion of Treg cells was increased in the high-risk group compared with the low-risk group. These results suggest that iron-metabolism could be involved in reshaping the TME, thereby disrupting the cell cycle of immune cells. This study utilized IRGs to construct a novel and reliable model, which can be used to assess the prognosis of patients with HCC and further clarify the molecular mechanisms of IRGs in HCC at single-cell resolution.

Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data

Objective

Classification tasks are an open challenge in the field of biomedicine. While several machine-learning techniques exist to accomplish this objective, several peculiarities associated with biomedical data, especially when it comes to omics measurements, prevent their use or good performance achievements. Omics approaches aim to understand a complex biological system through systematic analysis of its content at the molecular level. On the other hand, omics data are heterogeneous, sparse and affected by the classical "curse of dimensionality" problem, i.e. having much fewer observation, samples (n) than omics features (p). Furthermore, a major problem with multi-omics data is the imbalance either at the class or feature level. The objective of this work is to study whether feature extraction and/or feature selection techniques can improve the performances of classification machine-learning algorithms on omics measurements.

Methods

Among all omics, metabolomics has emerged as a powerful tool in cancer research, facilitating a deeper understanding of the complex metabolic landscape associated with tumorigenesis and tumor progression. Thus, we selected three publicly available metabolomics datasets, and we applied several feature extraction techniques both linear and non-linear, coupled or not with feature selection methods, and evaluated the performances regarding patient classification in the different configurations for the three datasets.

Results

We provide general workflow and guidelines on when to use those techniques depending on the characteristics of the data available. To further test the extension of our approach to other omics data, we have included a transcriptomics and a proteomics data. Overall, for all datasets, we showed that applying supervised feature selection improves the performances of feature extraction methods for classification purposes. Scripts used to perform all analyses are available at: https://github.com/Plant-Net/Metabolomic_project/.

Making commercial bracelet smarter with a biochemical button module

Despite the rapid development of mobile health based on wearable devices in recent years, lack of access to biochemical detection remains a vital challenge for most commercial wearable devices, which hinders the provision of effective electronic health records (EHRs) for disease control strategies, and further constraining the development of personalized precision medicine. Herein, we propose a strategy to graft biochemical detection function onto commercial bracelet. Different from the conventional development process of designing a completely new wearable biochemical device, we prefer to upgrade existing commercial wearable device to achieve simpler, faster, and more effective research and commercialization processes. An affordable and user-friendly biochemical button module has been designed that enables to integrate sensitive, specific, and rapid biochemical detection function into the idle space on the strap of the bracelet without increasing the size of the main body. This "Smart Bracelet Plus" shows the ability to simultaneously monitor physical and biochemical signals, and will serve as a reliable and systematic personal diagnostics and monitoring platform for providing real-time EHRs for disease control strategies and improving the efficiency of the healthcare system.

Anti-GluA3 autoantibodies define a new sub-population of frontotemporal lobar degeneration patients with distinct neuropathological features

Autoantibodies directed against the GluA3 subunit (anti-GluA3 hIgGs) of AMPA receptors have been identified in 20%-25% of patients with frontotemporal lobar degeneration (FTLD). Data from patients and in vitro/ex vivo pre-clinical studies indicate that anti-GluA3 hIgGs negatively affect glutamatergic neurotransmission. However, whether and how the chronic presence of anti-GluA3 hIgGs triggers synaptic dysfunctions and the appearance of FTLD-related neuropathological and behavioural signature has not been clarified yet. To address this question, we developed and characterized a pre-clinical mouse model of passive immunization with anti-GluA3 hIgGs purified from patients. In parallel, we clinically compared FTLD patients who were positive for anti-GluA3 hIgGs to negative ones. Clinical data showed that the presence of anti-GluA3 hIgGs defined a subgroup of patients with distinct clinical features. In the preclinical model, anti-GluA3 hIgGs administration led to accumulation of phospho-tau in the postsynaptic fraction and dendritic spine loss in the prefrontal cortex. Remarkably, the preclinical model exhibited behavioural disturbances that mostly reflected the deficits proper of patients positive for anti-GluA3 hIgGs. Of note, anti-GluA3 hIgGs-mediated alterations were rescued in the animal model by enhancing glutamatergic neurotransmission with a positive allosteric modulator of AMPA receptors. Overall, our study clarified the contribution of anti-GluA3 autoantibodies to central nervous system symptoms and pathology and identified a specific subgroup of FTLD patients. Our findings will be instrumental in the development of a therapeutic personalised medicine strategy for patients positive for anti-GluA3 hIgGs.

Precision Medicine in Veterinary Science

Precision medicine focuses on the clinical management of the individual patient, not on population-based findings. Successes from human precision medicine inform veterinary oncology. Early evidence of success for canines shows how precision medicine can be integrated into practice. Decreasing genomic profiling costs will allow increased utilization and subsequent improvement of knowledge base from which to make better informed decisions. Utility of precision medicine in canine oncology will only increase for improved cancer characterization, enhanced therapy selection, and overall more successful management of canine cancer. As such, practitioners are called to interpret and leverage precision medicine reports for their patients.

Patient-derived tumoroid models of pulmonary large-cell neuroendocrine carcinoma: a promising tool for personalized medicine and developing novel therapeutic strategies

Pulmonary large-cell neuroendocrine carcinoma (LCNEC), a disease with poor prognosis, is classified as pulmonary high-grade neuroendocrine carcinoma, along with small-cell lung cancer. However, given its infrequent occurrence, only a limited number of preclinical models have been established. Here, we established three LCNEC tumoroids for long-term culture. Whole-exome sequencing revealed that these tumoroids inherited genetic mutations from their parental tumors; two were classified as small-cell carcinoma (S-LCNEC) and one as non-small cell carcinoma (N-LCNEC). Xenografts from these tumoroids in immunodeficient mice mimicked the pathology of the parent LCNEC, and one reproduced the mixed-tissue types of combined LCNEC with a component of adenocarcinoma. Drug sensitivity tests using these LCNEC tumoroids enabled the evaluation of therapeutic agent efficacy. Based on translational research, we found that a CDK4/6 inhibitor might be effective for N-LCNEC and that Aurora A kinase inhibitors might be suitable for S-LCNEC or LCNEC with MYC amplification. These results highlight the value of preclinical tumoroid models in understanding the pathogenesis of rare cancers and developing treatments. LCNEC showed a high success rate in tumoroid establishment, indicating its potential application in personalized medicine.

Customizable orodispersible films: Inkjet printing and data matrix encoding for personalized hydrocortisone dosing

The aim of this study was to exploit the versatility of inkjet printing to develop flexible doses of drug-loaded orodispersible films that encoded information in a data matrix pattern, and to introduce a specialised data matrix-generator software specifically focused on the healthcare sector. Pharma-inks (drug-loaded inks) containing hydrocortisone (HC) were developed and characterised based on their rheological properties and drug content. Different strategies were investigated to improve HC solubility: formation of β-cyclodextrin complexes, Soluplus® based micelles, and the use of co-solvent systems. The software automatically adapted the data matrix size and identified the number of layers for printing. HC content deposited in each film layer was measured, and it was found that the proportion of co-solvent used directly affected the drug solubility and simultaneously played a role in the modification of the viscosity and surface tension of the inks. The formation of β-cyclodextrin complexes improved the drug quantity deposited in each layer. On the contrary, micelle-based inks were not suitable for printing. Orodispersible films containing flexible and low doses of personalised HC were successfully prepared, and the development of a code generator software oriented to medical use provided an additional, innovative, and revolutionary advantage to personalised medicine safety and accessibility.

Application of 3D printing technology for the development of dose adjustable geriatric and pediatric formulation of celecoxib

Developing safe and effective formulations for the geriatric and pediatric population is a challenging task due to issues of swallowability and palatability. The lack of standardized procedures for pediatric formulations further complicates the process. Manipulating adult formulations for children can lead to suboptimal efficacy and safety concerns. To overcome these challenges, minitablets or spinklets are preferred for the geriatric and pediatric population due to their smaller size and flexible dose adjustment. The aim of this study is the development of a 3D printed spinklets formulation of celecoxib, a nonsteroidal anti-inflammatory drug, using hot melt extrusion to address the limitations of traditional manufacturing methods. Three different formulations of celecoxib were prepared using Poly-2-ethyl-tetra-oxazoline (Aquazol) with and without surfactant. Subsequently, the mechanical properties and solubility of the drug-loaded filaments were evaluated. Solid state characterization confirmed the drug conversion into an amorphous form during the extrusion process, Computer-aided design software facilitate sprinklets design for fused deposition modeling and scanning electron microscopy assess the surface morphology. Sophorolipids plasticize better than TPGS, resulting in lowering processing temperatures during melt extrusion. In vitro drug release showed successful enhancements in the dissolution of oral medications for pediatric patients, considering their distinctive physiological characteristics. Overall, this study demonstrates the successful development of PEtOx-based 3D printed celecoxib sprinklets by coupling hot-melt extrusion and 3D printing technology. Future exploration holds the potential to revolutionize pharmaceutical production and advance personalized medication formulations.

Rheological insights into 3D printing of drug products: Drug nanocrystal-poloxamer gels for semisolid extrusion

The importance of ink rheology to the outcome of 3D printing is well recognized. However, rheological properties of printing inks containing drug nanocrystals have not been widely investigated. Therefore, the objective of this study was to establish a correlation between the composition of nanocrystal printing ink, the ink rheology, and the entire printing process. Indomethacin was used as a model poorly soluble drug to produce nanosuspensions with improved solubility properties through particle size reduction. The nanosuspensions were further developed into semisolid extrusion 3D printing inks with varying nanocrystal and poloxamer 407 concentrations. Nanocrystals were found to affect the rheological properties of the printing inks both by being less self-supporting and having higher yielding resistances. During printing, nozzle blockages occurred. Nevertheless, all inks were found to be printable. Finally, the rheological properties of the inks were successfully correlated with various printing and product properties. Overall, these experiments shed new light on the rheological properties of printing inks containing nanocrystals.

Treating sex and gender differences as a continuous variable can improve precision cancer treatments

BACKGROUND

The significant sex and gender differences that exist in cancer mechanisms, incidence, and survival, have yet to impact clinical practice. One barrier to translation is that cancer phenotypes cannot be segregated into distinct male versus female categories. Instead, within this convenient but contrived dichotomy, male and female cancer phenotypes are highly overlapping and vary between female- and male- skewed extremes. Thus, sex and gender-specific treatments are unrealistic, and our translational goal should be adaptation of treatment to the variable effects of sex and gender on targetable pathways.

METHODS

To overcome this obstacle, we profiled the similarities in 8370 transcriptomes of 26 different adult and 4 different pediatric cancer types. We calculated the posterior probabilities of predicting patient sex and gender based on the observed sexes of similar samples in this map of transcriptome similarity.

RESULTS

Transcriptomic index (TI) values were derived from posterior probabilities and allowed us to identify poles with local enrichments for male or female transcriptomes. TI supported deconvolution of transcriptomes into measures of patient-specific activity in sex and gender-biased, targetable pathways. It identified sex and gender-skewed extremes in mechanistic phenotypes like cell cycle signaling and immunity, and precisely positioned each patient's whole transcriptome on an axis of continuously varying sex and gender phenotypes.

CONCLUSIONS

Cancer type, patient sex and gender, and TI value provides a novel and patient- specific mechanistic identifier that can be used for realistic sex and gender-adaptations of precision cancer treatment planning.

Next-generation sequencing of host genetics risk factors associated with COVID-19 severity and long-COVID in Colombian population

Coronavirus disease 2019 (COVID-19) was considered a major public health burden worldwide. Multiple studies have shown that susceptibility to severe infections and the development of long-term symptoms is significantly influenced by viral and host factors. These findings have highlighted the potential of host genetic markers to identify high-risk individuals and develop target interventions to reduce morbimortality. Despite its importance, genetic host factors remain largely understudied in Latin-American populations. Using a case-control design and a custom next-generation sequencing (NGS) panel encompassing 81 genetic variants and 74 genes previously associated with COVID-19 severity and long-COVID, we analyzed 56 individuals with asymptomatic or mild COVID-19 and 56 severe and critical cases. In agreement with previous studies, our results support the association between several clinical variables, including male sex, obesity and common symptoms like cough and dyspnea, and severe COVID-19. Remarkably, thirteen genetic variants showed an association with COVID-19 severity. Among these variants, rs11385942 (p < 0.01; OR = 10.88; 95% CI = 1.36-86.51) located in the LZTFL1 gene, and rs35775079 (p = 0.02; OR = 8.53; 95% CI = 1.05-69.45) located in CCR3 showed the strongest associations. Various respiratory and systemic symptoms, along with the rs8178521 variant (p < 0.01; OR = 2.51; 95% CI = 1.27-4.94) in the IL10RB gene, were significantly associated with the presence of long-COVID. The results of the predictive model comparison showed that the mixed model, which incorporates genetic and non-genetic variables, outperforms clinical and genetic models. To our knowledge, this is the first study in Colombia and Latin-America proposing a predictive model for COVID-19 severity and long-COVID based on genomic analysis. Our study highlights the usefulness of genomic approaches to studying host genetic risk factors in specific populations. The methodology used allowed us to validate several genetic variants previously associated with COVID-19 severity and long-COVID. Finally, the integrated model illustrates the importance of considering genetic factors in precision medicine of infectious diseases.

Tumor Mutational Burden in Colorectal Cancer: Implications for Treatment

Although immune checkpoint inhibitors have revolutionized the treatment of several advanced solid cancers, in colorectal cancer, the transformative benefit of these innovative medicines is currently limited to those with deficient mismatch repair or high microsatellite instability. Tumor mutational burden (TMB) has emerged as a potential predictor of immunotherapy benefit, but the lack of standardization in its assessment and reporting has hindered the introduction of this biomarker in routine clinical practice. Here, we compiled 45 colorectal cancer studies utilizing numerical thresholds for high-TMB. In this group of studies, TMB cut-offs ranged from 6.88 to 41 mut/Mb and were most often set at 10, 17, or 20 mut/Mb. Additionally, we observed divergent TMB definitions and inconsistent disclosure of specific methodological details, which collectively emphasize the substantial lack of harmonization within the field. Ongoing efforts to harmonize TMB assessment will be critical to validate TMB as a predictive marker of immunotherapy response.

Cancer resistance and metastasis are maintained through oxidative phosphorylation

Malignant tumors have increased energy requirements due to growth, differentiation or response to stress. A significant number of studies in recent years have described upregulation of mitochondrial genes responsible for oxidative phosphorylation (OXPHOS) in some tumors. Although OXPHOS is replaced by glycolysis in some tumors (Warburg effect), both processes can occur simultaneously during the evolution of the same malignancies. In particular, chemoresistant and/or cancer stem cells appear to find a way to activate OXPHOS and metastasize. In this paper, we discuss recent work showing upregulation of OXPHOS in chemoresistant tumors and cell models. In addition, we show an inverse correlation of OXPHOS gene expression with the survival time of cancer patients after chemotherapy and discuss combination therapies for resistant tumors.

Medical, pharmaceutical, and nutritional applications of 3D-printing technology in diabetes

AIMS

Despite numerous studies covering the various features of three-dimensional printing (3D printing) technology, and its applications in food science and disease treatment, no study has yet been conducted to investigate applying 3D printing in diabetes. Therefore, the present study centers on the utilization and impact of 3D printing technology in relation to the nutritional, pharmaceutical, and medicinal facets of diabetes management. It highlights the latest advancements, and challenges in this field.

METHODS

In this review, the articles focusing on the application and effect of 3D printing technology on medical, pharmaceutical, and nutritional aspects of diabetes management were collected from different databases.

RESULT

High precision of 3D printing in the placement of cells led to accurate anatomic control, and the possibility of bio-printing pancreas and β-cells. Transdermal drug delivery via 3D-printed microneedle (MN) patches was beneficial for the management of diabetes disease. 3D printing supported personalized medicine for Diabetes Mellitus (DM). For instance, it made it possible for pharmaceutical companies to manufacture unique doses of medications for every diabetic patient. Moreover, 3D printing allowed the food industry to produce high-fiber and sugar-free products for the individuals with DM.

CONCLUSIONS

In summary, applying 3D printing technology for diabetes management is in its early stages, and needs to be matured and developed to be safely used for humans. However, its rapid progress in recent years showed a bright future for the treatment of diabetes.

Towards accurate indel calling for oncopanel sequencing through an international pipeline competition at precisionFDA

Accurately calling indels with next-generation sequencing (NGS) data is critical for clinical application. The precisionFDA team collaborated with the U.S. Food and Drug Administration's (FDA's) National Center for Toxicological Research (NCTR) and successfully completed the NCTR Indel Calling from Oncopanel Sequencing Data Challenge, to evaluate the performance of indel calling pipelines. Top performers were selected based on precision, recall, and F1-score. The performance of many other pipelines was close to the top performers, which produced a top cluster of performers. The performance was significantly higher in high confidence regions and coding regions, and significantly lower in low complexity regions. Oncopanel capture and other issues may have occurred that affected the recall rate. Indels with higher variant allele frequency (VAF) may generally be called with higher confidence. Many of the indel calling pipelines had good performance. Some of them performed generally well across all three oncopanels, while others were better for a specific oncopanel. The performance of indel calling can further be improved by restricting the calls within high confidence intervals (HCIs) and coding regions, and by excluding low complexity regions (LCR) regions. Certain VAF cut-offs could be applied according to the applications.

Regeneration of Volumetric Muscle Loss Using MSCs Encapsulated in PRP-Derived Fibrin Microbeads

Volumetric muscle loss (VML) is one of the major types of soft tissue injury frequently encountered worldwide. In case of VML, the endogenous regenerative capacity of the skeletal muscle tissue is usually not sufficient for complete healing of the damaged area resulting in permanent functional musculoskeletal impairment. Therefore, the development of new tissue engineering approaches that will enable functional skeletal muscle regeneration by overcoming the limitations of current clinical treatments for VML injuries has become a critical goal. Platelet-rich plasma (PRP) is an inexpensive and relatively effective blood product with a high concentration of platelets containing various growth factors and cytokines involved in wound healing and tissue regeneration. Due to its autologous nature, PRP has been a safe and widely used treatment option for various wound types for many years. Recently, PRP-based biomaterials have emerged as a promising approach to promote muscle tissue regeneration upon injury. This chapter describes the use of PRP-derived fibrin microbeads as a versatile encapsulation matrix for the localized delivery of mesenchymal stem cells and growth factors to treat VML using tissue engineering strategies.

Ethical use of artificial intelligence to prevent sudden cardiac death: an interview study of patient perspectives

BACKGROUND

The emergence of artificial intelligence (AI) in medicine has prompted the development of numerous ethical guidelines, while the involvement of patients in the creation of these documents lags behind. As part of the European PROFID project we explore patient perspectives on the ethical implications of AI in care for patients at increased risk of sudden cardiac death (SCD).

AIM

Explore perspectives of patients on the ethical use of AI, particularly in clinical decision-making regarding the implantation of an implantable cardioverter-defibrillator (ICD).

METHODS

Semi-structured, future scenario-based interviews were conducted among patients who had either an ICD and/or a heart condition with increased risk of SCD in Germany (n = 9) and the Netherlands (n = 15). We used the principles of the European Commission's Ethics Guidelines for Trustworthy AI to structure the interviews.

RESULTS

Six themes arose from the interviews: the ability of AI to rectify human doctors' limitations; the objectivity of data; whether AI can serve as second opinion; AI explainability and patient trust; the importance of the 'human touch'; and the personalization of care. Overall, our results reveal a strong desire among patients for more personalized and patient-centered care in the context of ICD implantation. Participants in our study express significant concerns about the further loss of the 'human touch' in healthcare when AI is introduced in clinical settings. They believe that this aspect of care is currently inadequately recognized in clinical practice. Participants attribute to doctors the responsibility of evaluating AI recommendations for clinical relevance and aligning them with patients' individual contexts and values, in consultation with the patient.

CONCLUSION

The 'human touch' patients exclusively ascribe to human medical practitioners extends beyond sympathy and kindness, and has clinical relevance in medical decision-making. Because this cannot be replaced by AI, we suggest that normative research into the 'right to a human doctor' is needed. Furthermore, policies on patient-centered AI integration in clinical practice should encompass the ethics of everyday practice rather than only principle-based ethics. We suggest that an empirical ethics approach grounded in ethnographic research is exceptionally well-suited to pave the way forward.

Data-driven personalized medicine approaches to cognitive-behavioral therapy allocation in a large sample: A reanalysis of the ENRICHED study

BACKGROUND

Although effective treatments for common mental health problems are available, individual responses to treatments are difficult to predict. Treatment efficacy could be optimized by targeting interventions using individual predictions of treatment outcomes. The aim of this study was to develop a prediction algorithm using data from one of the largest randomized controlled trials on psychological interventions for common mental health problems.

METHODS

This is a secondary analysis of the Enhancing Recovery in Coronary Heart Disease study investigating the effectiveness of cognitive behavioral therapy (CBT) and care as usual (CAU) for depression and low perceived social support following acute myocardial infarction. 2481 participants were randomly assigned to CBT and CAU. Baseline social-demographics, depression characteristics, comorbid symptoms, and stress and adversity measures were used to build an algorithm predicting post-treatment depression severity using elastic net regularization. Performance and generalizability of this algorithm were determined in a hold-out sample (n = 1203).

RESULTS

Treatment matching based on predictions in the hold-out sample resulted in inconsistent and small effects (d = 0.15), that were more pronounced for individuals matched to CBT (d = 0.22). We identified a small subgroup of individuals for which CBT did not appear more efficacious than CAU.

LIMITATIONS

Limitations are a poorly defined CAU condition, a low-severity sample, specific exclusion criteria and unavailability of certain baseline variables.

CONCLUSIONS

Small matching effects are likely a realistic representation of the performance and generalizability of multivariable prediction algorithms based on clinical measures. Results indicate that future work and new approaches are needed.

3D printing processes in precise drug delivery for personalized medicine

With the advent of personalized medicine, the drug delivery system (DDS) will be changed significantly. The development of personalized medicine needs the support of many technologies, among which three-dimensional printing (3DP) technology is a novel formulation-preparing process that creates 3D objects by depositing printing materials layer-by-layer based on the computer-aided design (CAD) method. Compared with traditional pharmaceutical processes, 3DP produces complex drug combinations, personalized dosage, and flexible shape and structure of dosage forms (DFs) on demand. In the future, personalized 3DP drugs may supplement and even replace their traditional counterpart. We systematically introduce the applications of 3DP technologies in the pharmaceutical industry and summarize the virtues and shortcomings of each technique. The release behaviors and control mechanisms of the pharmaceutical DFs with desired structures are also analyzed. Finally, the benefits, challenges, and prospects of 3DP technology to the pharmaceutical industry are discussed.

Preclinical Atopic Dermatitis Skin in Infants: An Emerging Research Area

Whereas clinically apparent atopic dermatitis (AD) can be confirmed by validated diagnostic criteria, the preclinical phenotype of infants who eventually develop AD is less well-characterized. Analogous to unaffected or nonlesional skin in established AD, clinically normal-appearing skin in infants who will develop clinical AD has distinct changes. Prospective studies have revealed insights into this preclinical AD phenotype. In this study, we review the structural, immunologic, and microbiome nature of the preclinical AD phenotype. Determination of markers that predict the development of AD will facilitate targeting of interventions to prevent the development or reduce the severity of AD in infants.

Precision Oncology in Breast Cancer Surgery

Outcomes for patients with breast cancer have improved over time due to increased screening and the availability of more effective therapies. It is important to recognize that breast cancer is a heterogeneous disease that requires treatment based on molecular characteristics. Early endpoints such as pathologic complete response correlate with event-free survival, allowing the opportunity to consider de-escalation of certain cancer treatments to avoid overtreatment. This article discusses clinical trials of tailoring treatment (eg, I-SPY2) and screening (eg, WISDOM) to individual patients based on their unique risk features.

An Integrated Care Approach to Improve Well-Being in Breast Cancer Patients

BACKGROUND

Breast cancer (BC) treatment has recently been revolutionized by the introduction of newer targeted agents, that helped tailoring therapies around the single patient. Along with increased survival rates, a careful evaluation of diet, lifestyle habits, physical activity, emotional and psychological experiences linked to the treatment journey, is now mandatory. However, a true proposal for an omnicomprehensive and "integrative" approach is still lacking in literature.

METHODS

A scientific board of internationally recognized specialists throughout different disciplines designed a shared proposal of holistic approach for BC patients.

RESULTS

A narrative review, containing information on BC treatment, endocrinological and diet aspects, physical activity, rehabilitation, integrative medicine, and digital narrative medicine, was developed.

CONCLUSIONS

In the context of a patient-centered care, BC treatment cannot be separated from a patient's long-term follow-up and care, and an organized interdisciplinary collaboration is the future in this disease's cure, to make sure that our patients will live longer and better.

TRIAL REGISTRATION

NCT05893368: New Model for Integrating Person-based Care (PbC) in the Treatment of Advanced HER2-negative Breast Cancer (PERGIQUAL). Registration date: 29th May 2023.

Gastric cancer patient-derived organoids model for the therapeutic drug screening

BACKGROUND

Gastric cancer (GC) is a highly heterogeneous disease featuring many various histological and molecular subtypes. Therefore, it is imperative to have well-characterized in vitro models for personalized treatment development. Gastric cancer patient-derived organoids (PDOs), re-capitulating in vivo conditions, exhibit high clinical efficacy in predicting drug sensitivity to facilitate the development of cancer precision medicine.

METHODS

PDOs were established from surgically resected GC tumor tissues. Histological and molecular characterization of PDOs and primary tissues were performed via IHC and sequencing analysis. We also conducted drug sensitivity tests using PDO cultures with five chemotherapeutic drugs and twenty-two targeted drugs.

RESULTS

We have successfully constructed a PDOs biobank that included EBV+, intestinal/CIN, diffuse/GS, mixed and Her2+ GC subtypes, and these PDOs captured the pathological and genetic characteristics of corresponding tumors and exhibited different sensitivities to the tested agents. In a clinical case study, we performed an additional drug sensitivity test for a patient who reached an advanced progressive stage after surgery. We discovered that the combination of napabucasin and COTI-2 exhibited a stronger synergistic effect than either drug alone.

CONCLUSION

PDOs maintained the histological and genetic characteristics of original cancer tissues. PDOs biobank opens up new perspectives for studying cancer cell biology and personalized medicine as a preclinical study platform.

Patients' selection and trial matching in early-phase oncology clinical trials

BACKGROUND

Early-phase clinical trials (EPCT) represent an important part of innovations in medical oncology and a valuable therapeutic option for patients with metastatic cancers, particularly in the era of precision medicine. Nevertheless, adult patients' participation in oncology clinical trials is low, ranging from 2% to 8% worldwide, with unequal access, and up to 40% risk of early discontinuation in EPCT, mostly due to cancer-related complications.

DESIGN

We review the tools and initiatives to increase patients' orientation and access to early phase cancer clinical trials, and to limit early discontinuation.

RESULTS

New approaches to optimize the early-phase clinical trial referring process in oncology include automatic trial matching, tools to facilitate the estimation of patients' prognostic and/or to better predict patients' eligibility to clinical trials. Classical and innovative approaches should be associated to double patient recruitment, improve clinical trial enrollment experience and reduce early discontinuation rates.

CONCLUSIONS

Whereas EPCT are essential for patients to access the latest medical innovations in oncology, offering the appropriate trial when it is relevant for patients should increase by organizational and technological innovations. The oncologic community will need to closely monitor their performance, portability and simplicity for implementation in daily clinical practice.

Elevated risk of recurrence and retreatment for silent pituitary adenomas

PURPOSE

Pituitary adenomas are the most common tumor of the pituitary gland and comprise nearly 15% of all intracranial masses. These tumors are stratified into functional or silent categories based on their pattern of hormone expression and secretion. Preliminary evidence supports differential clinical outcomes between some functional pituitary adenoma (FPA) subtypes and silent pituitary adenoma (SPA) subtypes.

METHODS

We collected and analyzed the medical records of all patients undergoing resection of SPAs or FPAs from a single high-volume neurosurgeon between 2007 and 2018 at Brigham and Women's Hospital. Descriptive statistics and the Mantel-Cox log-rank test were used to identify differences in outcomes between these cohorts, and multivariate logistic regression was used to identify predictors of radiographic recurrence for SPAs.

RESULTS

Our cohort included 88 SPAs and 200 FPAs. The majority of patients in both cohorts were female (48.9% of SPAs and 63.5% of FPAs). SPAs were larger in median diameter than FPAs (2.1 cm vs. 1.2 cm, p < 0.001). The most frequent subtypes of SPA were gonadotrophs (55.7%) and corticotrophs (30.7%). Gross total resection (GTR) was achieved in 70.1% of SPA resections and 86.0% of FPA resections (p < 0.001). SPAs had a higher likelihood of recurring (hazard ratio [HR] 3.2, 95% confidence interval [95%CI] 1.6-7.2) and a higher likelihood of requiring retreatment for recurrence (HR 2.5; 95%CI 1.0-6.1). Subset analyses revealed that recurrence and retreatment were more both likely for subtotally resected SPAs than subtotally resected FPAs, but this pattern was not observed in SPAs and FPAs after GTR. Among SPAs, recurrence was associated with STR (odds ratio [OR] 9.3; 95%CI 1.4-64.0) and younger age (OR 0.92 per year; 95%CI 0.88-0.98) in multivariable analysis. Of SPAs that recurred, 12 of 19 (63.2%) were retreated with repeat surgery (n = 11) or radiosurgery (n = 1), while the remainder were observed (n = 7).There were similar rates of recurrence across different SPA subtypes.

CONCLUSION

Patients undergoing resection of SPAs should be closely monitored for disease recurrence through more frequent clinical follow-up and diagnostic imaging than other adenomas, particularly among patients with STR and younger patients. Several patients can be observed after radiographic recurrence, and the decision to retreat should be individualized. Longitudinal clinical follow-up of SPAs, including an assessment of symptoms, endocrine function, and imaging remains critical.

Evolution of Precision Oncology, Personalized Medicine, and Molecular Tumor Boards

With multiple molecular targeted therapies available for patients with cancer that correspond to a specific genetic alteration, the selection of the best treatment is essential to ensure therapeutic efficacy. Molecular tumor boards (MTBs) play a key role in this process to deliver personalized medicine to patients with cancer in a multidisciplinary manner. Historically, personalized medicine has been offered to patients with advanced cancer, but the incorporation of molecular targeted therapies and immunotherapy into the perioperative setting requires clinicians to understand the role of the MTB. Evidence is accumulating to support feasibility and survival benefit in patients treated with matched therapy.

Implications of Highly Penetrant Genetic Variants on Breast Surgery

It seems the most probable beneficiaries from the molecular era are those harboring hereditary genetic variants, which are responsible for 5% to 10% of all breast cancer diagnoses. There are several key implications of such variants on clinical practice, from expedited anticipation of primary cancer diagnoses, which can have their risk mitigated by risk reduction surgery, to pragmatism surrounding the management of male breast cancer patients. This communication discusses the implications of highly penetrant (or pathogenic) hereditary variants in contemporary breast surgery practice.

The study of primary and acquired resistance to first-line osimertinib to improve the outcome of EGFR-mutated advanced Non-small cell lung cancer patients: the challenge is open for new therapeutic strategies

The development of targeted therapy in epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer (NSCLC) patients has radically changed their clinical perspectives. Current first-line standard treatment for advanced disease is commonly considered third-generation tyrosine kinase inhibitors (TKI), osimertinib. The study of primary and acquired resistance to front-line osimertinib is one of the main burning issues to further improve patients' outcome. Great heterogeneity has been depicted in terms of duration of clinical benefit and pattern of progression and this might be related to molecular factors including subtypes of EGFR mutations and concomitant genetic alterations. Acquired resistance can be categorized into two main classes: EGFR-dependent and EGFR-independent mechanisms and specific pattern of progression to first-line osimertinib have been demonstrated. The purpose of the manuscript is to provide a comprehensive overview of literature about molecular resistance mechanisms to first-line osimertinib, from a clinical perspective and therefore in relationship to emerging therapeutic approaches.

Ex vivo drug testing of patient-derived lung organoids to predict treatment responses for personalized medicine

Lung cancer is the leading cause of global cancer-related mortality resulting in ∼ 1.8 million deaths annually. Systemic, molecular targeted, and immune therapies have provided significant improvements of survival outcomes for patients. However, drug resistance usually arises and there is an urgent need for novel therapy screening and personalized medicine. 3D patient-derived organoid (PDO) models have emerged as a more effective and efficient alternative for ex vivo drug screening than 2D cell culture and patient-derived xenograft (PDX) models. In this review, we performed an extensive search of lung cancer PDO-based ex vivo drug screening studies. Lung cancer PDOs were successfully established from fresh or bio-banked sections and/or biopsies, pleural effusions and PDX mouse models. PDOs were subject to ex vivo drug screening with chemotherapy, targeted therapy and/or immunotherapy. PDOs consistently recapitulated the genomic alterations and drug sensitivity of primary tumors. Although sample sizes of the previous studies were limited and some technical challenges remain, PDOs showed great promise in the screening of novel therapy drugs. With the technical advances of high throughput, tumor-on-chip, and combined microenvironment, the drug screening process using PDOs will enhance precision care of lung cancer patients.

A personalized medicine approach identifies enasidenib as an efficient treatment for IDH2 mutant chondrosarcoma

BACKGROUND

Sarcomas represent an extensive group of malignant diseases affecting mesodermal tissues. Among sarcomas, the clinical management of chondrosarcomas remains a complex challenge, as high-grade tumours do not respond to current therapies. Mutations in the isocitrate dehydrogenase (IDH) 1 and 2 genes are among the most common mutations detected in chondrosarcomas and may represent a therapeutic opportunity. The presence of mutated IDH (mIDH) enzymes results in the accumulation of the oncometabolite 2-HG leading to molecular alterations that contribute to drive tumour growth.

METHODS

We developed a personalized medicine strategy based on the targeted NGS/Sanger sequencing of sarcoma samples (n = 6) and the use of matched patient-derived cell lines as a drug-testing platform. The anti-tumour potential of IDH mutations found in two chondrosarcoma cases was analysed in vitro, in vivo and molecularly (transcriptomic and DNA methylation analyses).

FINDINGS

We treated several chondrosarcoma models with specific mIDH1/2 inhibitors. Among these treatments, only the mIDH2 inhibitor enasidenib was able to decrease 2-HG levels and efficiently reduce the viability of mIDH2 chondrosarcoma cells. Importantly, oral administration of enasidenib in xenografted mice resulted in a complete abrogation of tumour growth. Enasidenib induced a profound remodelling of the transcriptomic landscape not associated to changes in the 5 mC methylation levels and its anti-tumour effects were associated with the repression of proliferative pathways such as those controlled by E2F factors.

INTERPRETATION

Overall, this work provides preclinical evidence for the use of enasidenib to treat mIDH2 chondrosarcomas.

FUNDING

Supported by the Spanish Research Agency/FEDER (grants PID2022-142020OB-I00; PID2019-106666RB-I00), the ISC III/FEDER (PI20CIII/00020; DTS18CIII/00005; CB16/12/00390; CB06/07/1009; CB19/07/00057); the GEIS group (GEIS-62); and the PCTI (Asturias)/FEDER (IDI/2021/000027).

Circulating tumor cells as liquid biopsy markers in cancer patients

Over the past decade, novel methods for enrichment and identification of cancer cells circulating in the blood have been established. Blood-based detection of cancer cells and other tumor-associated products can be summarized under the term of Liquid Biopsy. Circulating tumor cells (CTCs) have been used for diagnosis, risk stratification and treatment selection as well as treatment monitoring in several studies over the past years, thus representing a valuable biomarker for cancer patients. A plethora of methods to enrich, detect and analyze CTCs has been established. In contrast to other liquid biopsy analytes (e.g. ctDNA), CTCs represent a viable analyte that provides a unique opportunity to understand the underlaying biology of cancer and the metastatic cascade on the molecular level. In this review, we provide an overview on the current methods used for enrichment, detection, molecular and functional characterization of CTCs.

Physical and cognitive correlates, inflammatory levels, and treatment response in post-COVID-19 first-onset vs. recurrent depressive episodes

Psychiatric symptoms have been frequently reported in patients affected by COVID-19, both as new occurring and recurrences of pre-existing diseases. Depressive symptoms are estimated to affect at least 30% of patients following infection, with specific physical and cognitive features and relevant immune-inflammatory alterations. This study aimed to retrospectively characterize post-COVID-19 first-onset and recurrent major depressive episodes (MDE) and to evaluate the effects of antidepressants on physical and cognitive correlates of depression, in addition to mood, anxiety, and underlying inflammatory status. We evaluated 116 patients (44.8% males, 51.1 ± 17 years) with post-COVID-19 first-onset (38.8%) and recurrent (61.2%) MDE at baseline and after one- and three-month treatment with antidepressants (31% SSRIs, 25.9% SNRIs, 43.1% others). We assessed sociodemographic and clinical features and psychopathological dimensions through: Hamilton Depression and Anxiety Rating Scales; Short Form-36 Health Survey Questionnaire; Perceived Deficits Questionnaire-Depression 5-items. The systemic immune-inflammatory index was calculated to measure inflammation levels. Alongside the reduction of depression and anxiety (p < 0.001), physical and cognitive symptoms improved (p < 0.001) and inflammatory levels decreased (p < 0.001) throughout treatment in both groups. Post-COVID-19 recurrent MDE showed a significantly more severe course of physical and cognitive symptoms and persistently higher levels of inflammation than first-onset episodes. Antidepressants proved to be effective in both post-COVID-19 first-onset and recurrent MDE. However, a sustained inflammatory status might blunt treatment response in patients with recurrent depression in terms of physical correlates and cognition. Therefore, personalized approaches, possibly involving combinations with anti-inflammatory compounds, could promote better outcomes in this clinical population.

Identifying subgroups with differential response to CBASP versus Escitalopram during the first eight weeks of treatment in outpatients with persistent depressive disorder

There exists little empirical evidence helping clinicians to select the most effective treatment for individual patients with persistent depressive disorder (PDD). This study identifies and characterizes subgroups of patients with PDD who are likely to benefit more from an acute treatment with psychotherapy than from pharmacotherapy and vice versa. Non-medicated outpatients with PDD were randomized to eight weeks of acute treatment with the Cognitive Behavioral Analysis System of Psychotherapy (CBASP; n = 29) or escitalopram plus clinical management (ESC/CM; n = 31). We combined several baseline variables to one composite moderator and identified two subgroups of patients: for 56.0%, ESC/CM was associated with a greater reduction in depression severity than CBASP, for the remaining 44.0%, it was the other way around. Patients likely to benefit more from ESC/CM were more often female, had higher rates of moderate-to-severe childhood trauma, more adverse life events and more previous suicide attempts. Patients likely to benefit more from CBASP were older, had more often an early illness onset and more previous treatments with antidepressants. Symptomatic response, remission, and reductions in symptom severity occurred more often in those patients treated with their likely more effective treatment condition. The findings suggest that the baseline phenotype of patients with PDD moderates their benefit from acute treatment with CBASP relative to ESC/CM. Once confirmed in an independent sample, these results could serve to guide the choice between primarily psychotherapeutic or pharmacological treatments for outpatients with PDD.

Ligamentous laxity in children with achondroplasia: Prevalence, joint involvement, and implications for early intervention strategies

Achondroplasia (ACH), the most common form of skeletal dysplasia, is characterized by severe disproportionate short stature, rhizomelia, exaggerated lumbar lordosis, brachydactyly, macrocephaly with frontal bossing and midface hypoplasia. Ligamentous laxity has been reported as a striking feature of ACH, but its prevalence and characteristics have not been systematically evaluated yet. There is growing evidence that ligamentous laxity can be associated with chronic musculoskeletal problems and may affect motor development leading to abnormal developmental trajectories. This study aimed to assess the prevalence of ligamentous laxity in children with ACH through standardized tools, the Beighton scale and its modified version for preschool-age children. A total of 33 children (mean age 6.4 ± 3.2 years; age range 1-12.5 years) diagnosed with ACH by the demonstration of a pathogenic variant in the FGFR3 gene and 33 age- and sex-matched healthy controls were included in the study. Both ligamentous laxity assessment and neurological examinations were performed; medical history was also collected from caregivers. Children with ACH showed a 2 times higher risk of ligamentous laxity than the group without skeletal dysplasia (OR = 2.2; 95% CI = 1.0 to 4.7), with 55% of children meeting the diagnostic criteria for hypermobility. No significant difference in ligamentous laxity was observed between males and females. Joint involvement analysis revealed characteristic patterns, with knee hypermobility observed in 67% of patients, while rare was elbow hypermobility. Longitudinal assessments indicated a decreasing trend in ligamentous laxity scores over time, suggesting a potential decrease in hypermobility issues during adulthood. The findings of this study provide valuable insights into the prevalence and characteristics of ligamentous laxity in ACH. Implementation of standardized ligamentous laxity assessments might guide patients' follow-up and facilitate early interventions, helping to prevent pain and improve outcomes and quality of life for such patients. Further prospective studies are needed to explore the natural history of ligamentous laxity in ACH and investigate the potential impact of emerging pharmacological treatments upon hypermobility.

The importance of protein domain mutations in cancer therapy

Cancer is a complex disease that is caused by multiple genetic factors. Researchers have been studying protein domain mutations to understand how they affect the progression and treatment of cancer. These mutations can significantly impact the development and spread of cancer by changing the protein structure, function, and signalling pathways. As a result, there is a growing interest in how these mutations can be used as prognostic indicators for cancer prognosis. Recent studies have shown that protein domain mutations can provide valuable information about the severity of the disease and the patient's response to treatment. They may also be used to predict the response and resistance to targeted therapy in cancer treatment. The clinical implications of protein domain mutations in cancer are significant, and they are regarded as essential biomarkers in oncology. However, additional techniques and approaches are required to characterize changes in protein domains and predict their functional effects. Machine learning and other computational tools offer promising solutions to this challenge, enabling the prediction of the impact of mutations on protein structure and function. Such predictions can aid in the clinical interpretation of genetic information. Furthermore, the development of genome editing tools like CRISPR/Cas9 has made it possible to validate the functional significance of mutants more efficiently and accurately. In conclusion, protein domain mutations hold great promise as prognostic and predictive biomarkers in cancer. Overall, considerable research is still needed to better define genetic and molecular heterogeneity and to resolve the challenges that remain, so that their full potential can be realized.

Exclusive Ru-106 brachytherapy for the management of a recurrent corneo: Conjunctival squamous cell carcinoma

BACKGROUND

We report a case of personalized exclusive brachytherapy treatment for the management of a highly recurrent squamous cell conjunctival carcinoma with corneal invasion.

CASE DESCRIPTION

This is a case of a Caucasian 81-years-old man who presented 10 years ago to our clinic with a pink-white para-limbal mass with dilated feeder arteries and mild leukoplakia. Excisional biopsy confirmed the presence of conjunctival intraepithelial neoplasia (CIN). Successively, he underwent two 4-weeks cycles of Mytomicin C topical therapy and a second excisional surgery, due to several recurrences of the lesion. At the last relapse, the pink-white peri-limbic mass which invaded the corneal limbus, determining corneal opacification from 5- to 7-clock hours, was confirmed by anterior segment optical coherence tomography (AS-OCT). Due to resistance to MMC therapy and chronic epitheliopathy, an AS-OCT guided exclusive radiotherapy plan was set: a Rhutenium-106 CCD plaque was applied directly over the afflicted corneal surface, the corneal limbus and the neighboring sclera for 24 hours. The remission of both conjunctival and corneal malignancy was complete 2 months after surgery and no signs of recurrence were highlighted at AS-OCT analysis at the 2-year follow up.

CONCLUSION

Brachytherapy treatment showed optimal management of both corneal and conjunctival involvement, with a free-of-disease follow-up of 24-months. This result suggests that, in specific conditions, Ru-106 brachytherapy could be an effective option of treatment even if not associated with surgical excision.

Advances and Applications of Cancer Organoids in Drug Screening and Personalized Medicine

In recent years, the rapid emergence of 3D organoid technology has garnered significant attention from researchers. These miniature models accurately replicate the structure and function of human tissues and organs, offering more physiologically relevant platforms for cancer research. These intricate 3D structures not only serve as promising models for studying human cancer, but also significantly contribute to the advancement of various potential applications in the field of cancer research. To date, organoids have been efficiently constructed from both normal and malignant tissues originating from patients. Using such bioengineering platforms, simulations of infections and cancer processes, mutations and carcinogenesis can be achieved, and organoid technology is also expected to facilitate drug testing and personalized therapies. In conclusion, regenerative medicine has the potential to enhance organoid technology and current transplantation treatments by utilizing genetically identical healthy organoids as substitutes for irreversibly deteriorating diseased organs. This review explored the evolution of cancer organoids and emphasized the significant role these models play in fundamental research and the advancement of personalized medicine in oncology.

Current prospects of hereditary adrenal tumors: towards better clinical management

Adrenocortical carcinoma (ACC) and pheochromocytoma/paraganglioma (PPGL) are two rare types of adrenal gland malignancies. Regarding hereditary tumors, some patients with ACC are associated with with Li-Fraumeni syndrome (LFS), and those with PPGL with multiple endocrine neoplasia type 2. Recent studies have expanded this spectrum to include other types of hereditary tumors, such as Lynch syndrome or familial adenomatous polyposis. Individuals harboring germline TP53 pathogenic variants that cause LFS have heterogeneous phenotypes depending on the respective variant type. As an example, R337H variant found in Brazilian is known as low penetrant. While 50-80% of pediatric ACC patients harbored a LFS, such a strong causal relationship is not observed in adult patients, which suggests different pathophysiologies between the two populations. As for PPGL, because multiple driver genes, such as succinate dehydrogenase (SDH)-related genes, RET, NF1, and VHL have been identified, universal multi-gene germline panel testing is warranted as a comprehensive and cost-effective approach. PPGL pathogenesis is divided into three molecular pathways (pseudohypoxia, Wnt signaling, and kinase signaling), and this classification is expected to result in personalized medicine based on genomic profiles. It remains unknown whether clinical characteristics differ between cases derived from genetic predisposition syndromes and sporadic cases, or whether the surveillance strategy should be changed depending on the genetic background or whether it should be uniform. Close cooperation among medical genomics experts, endocrinologists, oncologists, and early investigators is indispensable for improving the clinical management for multifaceted ACC and PPGL.

Estimating individualized treatment effects using an individual participant data meta-analysis

BACKGROUND

One key aspect of personalized medicine is to identify individuals who benefit from an intervention. Some approaches have been developed to estimate individualized treatment effects (ITE) with a single randomized control trial (RCT) or observational data, but they are often underpowered for the ITE estimation. Using individual participant data meta-analyses (IPD-MA) might solve this problem. Few studies have investigated how to develop risk prediction models with IPD-MA, and it remains unclear how to combine those methods with approaches used for ITE estimation. In this article, we compared different approaches using both simulated and real data with binary and time-to-event outcomes to estimate the individualized treatment effects from an IPD-MA in a one-stage approach.

METHODS

We compared five one-stage models: naive model (NA), random intercept (RI), stratified intercept (SI), rank-1 (R1), and fully stratified (FS), built with two different strategies, the S-learner and the T-learner constructed with a Monte Carlo simulation study in which we explored different scenarios with a binary or a time-to-event outcome. To evaluate the performance of the models, we used the c-statistic for benefit, the calibration of predictions, and the mean squared error. The different models were also used on the INDANA IPD-MA, comparing an anti-hypertensive treatment to no treatment or placebo ( N = 40 237 , 836 events).

RESULTS

Simulation results showed that using the S-learner led to better ITE estimation performances for both binary and time-to-event outcomes. None of the risk models stand out and had significantly better results. For the INDANA dataset with a binary outcome, the naive and the random intercept models had the best performances.

CONCLUSIONS

For the choice of the strategy, using interactions with treatment (the S-learner) is preferable. For the choice of the method, no approach is better than the other.

Tailoring drug release in bilayer tablets through droplet deposition modeling and injection molding

This study explores the innovative production of personalized bilayer tablets, integrating two advanced manufacturing techniques: Droplet Deposition Modeling (DDM) and Injection Molding (IM). Unlike traditional methods limited to customizing dense bilayer medicines, our approach uses Additive Manufacturing (AM) to effectively adjust drug release profiles. Focusing on Caffeine and Paracetamol, we found successful processing for both DDM and IM using Caffeine formulation. The high viscosity of Paracetamol formulation posed challenges during DDM processing. Integrating Paracetamol formulation for the over-molding process proved effective, demonstrating IM's versatility in handling complex formulations. Varying infill percentages in DDM tablets led to distinct porosities affecting diverse drug release profiles in DDM-fabricated tablets. In contrast, tablets with high-density structures formed through the over-molding process displayed slower and more uniform release patterns. Combining DDM and IM techniques allows for overcoming the inherent limitations of each technique independently, enabling the production of bilayer tablets with customizable drug release profiles. The study's results offer promising insights into the future of personalized medicine, suggesting new pathways for the development of customized oral dosage forms.

Development of multiple structured extended release tablets via hot melt extrusion and dual-nozzle fused deposition modeling 3D printing

The study aims to fabricate extended release (ER) tablets using a dual-nozzle fused deposition modeling (FDM) three-dimensional (3D) printing technology based on hot melt extrusion (HME), using caffeine as the model compound. Three different ER tablets were developed, which obtained "delayed-release", "rapid-sustained release", and "release-lag-release" properties. Each type of tablet was printed with two different formulations. A novel printing method was employed in this study, which is to push the HME filament from behind with polylactic acid (PLA) to prevent sample damage by gears during the printing process. Powder X-ray diffractometry (PXRD) and differential scanning calorimetry (DSC) results showed that caffeine was predominately amorphous in the final tablets. The dissolution of 3D printed tablets was assessed using a USP-II dissolution apparatus. ER tablets containing PVA dissolved faster than those developed with Kollicoat IR. Overall, this study revealed that ER tablets were successfully manufactured through HME paired with dual-nozzle FDM 3D printing and demonstrated the power of 3D printing in developing multi-layer tablets with complex structures.

Bridging the gap between testing and clinics exploring alternative pre-clinical models in melanoma research

Melanoma, the deadliest form of skin cancer, poses a significant clinical challenge for the development of effective treatments. Conventional in vivo animal studies have shown limited translational relevance to humans, raising strength to pre-clinical models for melanoma research. This review provides an in-depth analysis of alternative pre-clinical models including in vitro and ex vivo platforms such as reconstructed skin, spheroids, organoids, organotypic models, skin-on-a-chip, and bioprinting. Through a comprehensive analysis, the specific attributes, advantages, and limitations of each model are elucidated. It discusses the points related to the uniqueness advantages, from capturing complex interactions between melanoma cells and their microenvironment to enabling high-throughput drug screening and personalized medicine approaches. This review is structured covering firstly the roadmap to identify the co-occurrence of discovering new melanoma treatments and the development of its models, secondly it covers a comparative between the most used models followed by a section discussing each of them: the in vitro and ex vivo models. It intends to serve as an asset for researchers of melanoma field and clinicians involved in melanoma therapy, offering insights into the diverse preclinical models available for optimizing their integration into the translational pipeline.

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine

INTRODUCTION

Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties.

AREAS COVERED

The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged.

EXPERT OPINION

AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.

AUD in perspective

Alcohol is a major cause of pre-mature death and individual suffering worldwide, and the importance of diagnosing and treating AUD cannot be overstated. Given the global burden and the high attributable factor of alcohol in a vast number of diseases, the need for additional interventions and the development of new medicines is considered a priority by the World Health Organization (WHO). As of today, AUD is severely under-treated with a treatment gap nearing 90%, strikingly higher than that for other psychiatric disorders. Patients often seek treatment late in the progress of the disease and even among those who seek treatment only a minority receive medication, mirroring the still-prevailing stigma of the disease, and a lack of access to effective treatments, as well as a reluctance to total abstinence. To increase adherence, treatment goals should focus not only on maintaining abstinence, but also on harm reduction and psychosocial functioning. A personalised approach to AUD treatment, with a holistic view, and tailored therapy has the potential to improve AUD treatment outcomes by targeting the heterogeneity in genetics and pathophysiology, as well as reason for, and reaction to drinking. Also, the psychiatric co-morbidity rates are high in AUD and dual diagnosis can worsen symptoms and influence treatment response and should be considered in the treatment strategies.

Revolutionizing cellular energy: The convergence of mitochondrial dynamics and delivery technologies

The intersection of mitochondrial dynamics and delivery technologies heralds a paradigm shift in cellular biology and therapeutic intervention. Mitochondrial dynamics, encompassing fusion, fission, transport, and mitophagy, are critical for cellular energy production, signaling, and homeostasis. Dysregulation of these processes is implicated in a myriad of diseases, including neurodegenerative disorders, cardiovascular diseases, and cancer. Concurrently, advances in delivery technologies, such as nanocarriers, targeted delivery systems, and gene editing tools, offer unprecedented opportunities to manipulate mitochondrial function directly. This review synthesizes current knowledge on mitochondrial dynamics, examines recent breakthroughs in targeted delivery methods, and explores their potential convergence to modulate cellular energetics for therapeutic purposes. By integrating insights from biology, chemistry, and bioengineering, this review highlights the innovative approaches being developed to enhance mitochondrial function, underscoring the potential of this convergence to address complex diseases. This interdisciplinary perspective not only broadens our understanding of cellular processes but also paves the way for novel therapeutic strategies, marking a significant step forward in the quest for precision medicine and targeted interventions in mitochondrial-related diseases.

Digital light 4D printing of bioresorbable shape memory elastomers for personalized biomedical implantation

Four-dimensional (4D) printing unlocks new potentials for personalized biomedical implantation, but still with hurdles of lacking suitable materials. Herein, we demonstrate a bioresorbable shape memory elastomer (SME) with high elasticity at both below and above its phase transition temperature (Ttrans). This SME can be digital light 3D printed by co-polymerizing glycerol dodecanoate acrylate prepolymer (pre-PGDA) with acrylic acid monomer to form crosslinked Poly(glycerol dodecanoate acrylate) (PGDA)-Polyacrylic acid (PAA), or PGDA-PAA network. The printed complex, free-standing 3D structures with high-resolution features exhibit shape programming properties at a physiological temperature. By tuning the pre-PGDA weight ratios between 55 wt% and 70 wt%, Ttrans varies between 39.2 and 47.2 ℃ while Young's moduli (E) range 40-170 MPa below Ttrans with fractural strain (εf) of 170 %-200 %. Above Ttrans, E drops to 1-1.82 MPa which is close to those of soft tissue. Strikingly, εf of 130-180 % is still maintained. In vitro biocompatibility test on the material shows > 90 % cell proliferation and great cell attachment. In vivo vascular grafting trials underline the geometrical and mechanical adaptability of these 4D printed constructs in regenerating the aorta tissue. Biodegradation of the implants shows the possibility of their full replacement by natural tissue over time. To highlight its potential for personalized medicine, a patient-specific left atrial appendage (LAA) occluder was printed and implanted endovascularly into an in vitro heart model. STATEMENT OF SIGNIFICANCE: 4D printed shape-memory elastomer (SME) implants particularly designed and manufactured for a patient are greatly sought-after in minimally invasive surgery (MIS). Traditional shape-memory polymers used in these implants often suffer from issues like unsuitable transition temperatures, poor biocompatibility, limited 3D design complexity, and low toughness, making them unsuitable for MIS. Our new SME, with an adjustable transition temperature and enhanced toughness, is both biocompatible and naturally degradable, particularly in cardiovascular contexts. This allows implants, like biomedical scaffolds, to be programmed at room temperature and then adapt to the body's physiological conditions post-implantation. Our studies, including in vivo vascular grafts and in vitro device implantation, highlight the SME's effectiveness in aortic tissue regeneration and its promising applications in MIS.

Longitudinal metabolite profiling of Streptococcus pneumoniae-associated community-acquired pneumonia

INTRODUCTION

Longitudinal biomarkers in patients with community-acquired pneumonia (CAP) may help in monitoring of disease progression and treatment response. The metabolic host response could be a potential source of such biomarkers since it closely associates with the current health status of the patient.

OBJECTIVES

In this study we performed longitudinal metabolite profiling in patients with CAP for a comprehensive range of metabolites to identify potential host response biomarkers.

METHODS

Previously collected serum samples from CAP patients with confirmed Streptococcus pneumoniae infection (n = 25) were used. Samples were collected at multiple time points, up to 30 days after admission. A wide range of metabolites was measured, including amines, acylcarnitines, organic acids, and lipids. The associations between metabolites and C-reactive protein (CRP), procalcitonin, CURB disease severity score at admission, and total length of stay were evaluated.

RESULTS

Distinct longitudinal profiles of metabolite profiles were identified, including cholesteryl esters, diacyl-phosphatidylethanolamine, diacylglycerols, lysophosphatidylcholines, sphingomyelin, and triglycerides. Positive correlations were found between CRP and phosphatidylcholine (34:1) (cor = 0.63) and negative correlations were found for CRP and nine lysophosphocholines (cor = - 0.57 to - 0.74). The CURB disease severity score was negatively associated with six metabolites, including acylcarnitines (tau = - 0.64 to - 0.58). Negative correlations were found between the length of stay and six triglycerides (TGs), especially TGs (60:3) and (58:2) (cor = - 0.63 and - 0.61).

CONCLUSION

The identified metabolites may provide insight into biological mechanisms underlying disease severity and may be of interest for exploration as potential treatment response monitoring biomarker.

Personalized decision support system for tailoring IgA nephropathy treatment strategies

BACKGROUND

The ongoing debate surrounding the use of immunosuppressive treatments for IgA nephropathy (IgAN) underscores the demand for personalized and effective strategies.

METHODS

Analyzed data from 807 IgAN patients over 5+ years using three methods: Random Forest with molecular biomarkers, network biomarkers with graph engineering, and an auto-encoder model. All models were trained using identical demographic, clinical, and pathological data, employing an 80-20 split for training and testing purposes.

RESULTS

In the comprehensive assessment of IgAN prognosis, the Random Forest model, employing molecular biomarkers, demonstrated strong performance metrics (AUC = 0.83, sensitivity = 0.51, specificity = 0.96). However, traditional graph feature engineering on patient-specific networks outperformed these results with an AUC of 0.90, sensitivity of 0.64, and specificity of 0.94. The Auto-encoder model showed the best accuracy (AUC = 0.91, sensitivity = 0.46, specificity = 0.96). The findings highlighted the superior predictive capabilities of network biomarkers over molecular biomarkers for adverse renal outcome prediction in IgAN. Consequently, we integrated Auto-encoder-derived Network Biomarkers with Random Forest Models to enhance prognostic precision in diverse IgAN treatment scenarios. The prediction for the prognosis of patients receiving supportive care, glucocorticoid therapy, and immunosuppressant treatment yielded AUC values of 0.95, 0.96, and 1, respectively, indicating high specificity. Drawing from these insights, we pioneered the development of an innovative decision support model for IgAN treatment. This model demonstrated the ability to make medical decisions comparable to those by experienced nephrologists, enabling the customization of personalized disease management strategies.

CONCLUSION

Our system accurately predicted IgAN prognosis and evaluated various treatment efficacies, aiding physicians in devising optimal therapeutic strategies for patients.

Systematic and quantitative analysis of stop codon readthrough in Rett syndrome nonsense mutations

Rett syndrome (RTT) is a neurodevelopmental disorder resulting from genetic mutations in the methyl CpG binding protein 2 (MeCP2) gene. Specifically, around 35% of RTT patients harbor premature termination codons (PTCs) within the MeCP2 gene due to nonsense mutations. A promising therapeutic avenue for these individuals involves the use of aminoglycosides, which stimulate translational readthrough (TR) by causing stop codons to be interpreted as sense codons. However, the effectiveness of this treatment depends on several factors, including the type of stop codon and the surrounding nucleotides, collectively referred to as the stop codon context (SCC). Here, we develop a high-content reporter system to precisely measure TR efficiency at different SCCs, assess the recovery of the full-length MeCP2 protein, and evaluate its subcellular localization. We have conducted a comprehensive investigation into the intricate relationship between SCC characteristics and TR induction, examining a total of 14 pathogenic MeCP2 nonsense mutations with the aim to advance the prospects of personalized therapy for individuals with RTT. Our results demonstrate that TR induction can successfully restore full-length MeCP2 protein, albeit to varying degrees, contingent upon the SCC and the specific position of the PTC within the MeCP2 mRNA. TR induction can lead to the re-establishment of nuclear localization of MeCP2, indicating the potential restoration of protein functionality. In summary, our findings underscore the significance of SCC-specific approaches in the development of tailored therapies for RTT. By unraveling the relationship between SCC and TR therapy, we pave the way for personalized, individualized treatment strategies that hold promise for improving the lives of individuals affected by this debilitating neurodevelopmental disorder. KEY MESSAGES: The efficiency of readthrough induction at MeCP2 premature termination codons strongly depends on the stop codon context. The position of the premature termination codon on the transcript influences the readthrough inducibility. A new high-content dual reporter assay facilitates the measurement and prediction of readthrough efficiency of specific nucleotide stop contexts. Readthrough induction results in the recovery of full-length MeCP2 and its re-localization to the nucleus. MeCP2 requires only one of its annotated nuclear localization signals.

Neuroimmune modulators as novel pharmacotherapies for substance use disorders

One promising avenue of research is the use of neuroimmune modulators to treat substance use disorders (SUDs). Neuroimmune modulators target the interactions between the nervous system and immune system, which have been found to play a crucial role in the development and maintenance of SUDs. Multiple classes of substances produce alterations to neuroimmune signaling and peripheral immune function, including alcohol, opioids, and psychostimulants Preclinical studies have shown that neuroimmune modulators can reduce drug-seeking behavior and prevent relapse in animal models of SUDs. Additionally, early-phase clinical trials have demonstrated the safety and feasibility of using neuroimmune modulators as a treatment for SUDs in humans. These therapeutics can be used as stand-alone treatments or as adjunctive. This review summarizes the current state of the field and provides future directions with a specific focus on personalized medicine.