A Quantitative Framework for Measuring Personalized Medicine Integration into US Healthcare Delivery Organizations.
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ABSTRACT:
SUBMITTER: Agarwal A
PROVIDER: S-EPMC8000405 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Response to the oncology drug gemcitabine may be variable in part due to genetic differences in the enzymes and transporters responsible for its metabolism and disposition. The aim of our in-silico study was to identify gene variants significantly associated with gemcitabine response that may help to personalize treatment in the clinic.We analyzed two independent data sets: (a) genotype data from NCI-60 cell lines using the Affymetrix DMET 1.0 platform combined with gemcitabine cytotoxicity data in those cell lines, and (b) genome-wide association studies (GWAS) data from 351 pancreatic cancer patients treated on an NCI-sponsored phase III clinical trial. We also performed a subset analysis on the GWAS data set for 135 patients who were given gemcitabine+placebo. Statistical and systems biology analyses were performed on each individual data set to identify biomarkers significantly associated with gemcitabine response.Genetic variants in the ABC transporters (ABCC1, ABCC4) and the CYP4 family members CYP4F8 and CYP4F12, CHST3, and PPARD were found to be significant in both the NCI-60 and GWAS data sets. We report significant association between drug response and variants within members of the chondroitin sulfotransferase family (CHST) whose role in gemcitabine response is yet to be delineated.Biomarkers identified in this integrative analysis may contribute insights into gemcitabine response variability. As genotype data become more readily available, similar studies can be conducted to gain insights into drug response mechanisms and to facilitate clinical trial design and regulatory reviews.
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Project description:Purpose: To evaluate what drives respondent perceptions of health system organizational cultural competence. Methods: We estimated associations between survey respondent (n=3506) demographic characteristics, length of employment, position, and place of work and their reported perceptions of institutional culture. Results: In adjusted analyses, respondents self-identifying as non-Hispanic black versus non-Hispanic whites, females versus males, and lesbian/gay/bisexual/transgender/queer versus heterosexuals were significantly less likely to rank the cultural competence of their organization above average. Conclusion: Minorities and women were less likely to rank their organization as culturally competent. Organizational efforts to achieve cultural competency would benefit from measuring this factor to target their efforts.
| S-EPMC6231484 | biostudies-literature
Project description:Background and objectivesMachine Learning offers opportunities to improve patient outcomes, team performance, and reduce healthcare costs. Yet only a small fraction of all Machine Learning models for health care have been successfully integrated into the clinical space. There are no current guidelines for clinical model integration, leading to waste, unnecessary costs, patient harm, and decreases in efficiency when improperly implemented. Systems engineering is widely used in industry to achieve an integrated system of systems through an interprofessional collaborative approach to system design, development, and integration. We propose a framework based on systems engineering to guide the development and integration of Machine Learning models in healthcare.MethodsApplied systems engineering, software engineering and health care Machine Learning software development practices were reviewed and critically appraised to establish an understanding of limitations and challenges within these domains. Principles of systems engineering were used to develop solutions to address the identified problems. The framework was then harmonized with the Machine Learning software development process to create a systems engineering-based Machine Learning software development approach in the healthcare domain.ResultsWe present an integration framework for healthcare Artificial Intelligence that considers the entirety of this system of systems. Our proposed framework utilizes a combined software and integration engineering approach and consists of four phases: (1) Inception, (2) Preparation, (3) Development, and (4) Integration. During each phase, we present specific elements for consideration in each of the three domains of integration: The Human, The Technical System, and The Environment. There are also elements that are considered in the interactions between these domains.ConclusionClinical models are technical systems that need to be integrated into the existing system of systems in health care. A systems engineering approach to integration ensures appropriate elements are considered at each stage of model design to facilitate model integration. Our proposed framework is based on principles of systems engineering and can serve as a guide for model development, increasing the likelihood of successful Machine Learning translation and integration.
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Project description:Clinical annotations are one of the most popular resources available on the Pharmacogenomics Knowledgebase (PharmGKB). Each clinical annotation summarizes the association between variant-drug pairs, shows relevant findings from the curated literature, and is assigned a level of evidence (LOE) to indicate the strength of support for that association. Evidence from the pharmacogenomic literature is curated into PharmGKB as variant annotations, which can be used to create new clinical annotations or added to existing clinical annotations. This means that the same clinical annotation can be worked on by multiple curators over time. As more evidence is curated into PharmGKB, the task of maintaining consistency when assessing all the available evidence and assigning an LOE becomes increasingly difficult. To remedy this, a scoring system has been developed to automate LOE assignment to clinical annotations. Variant annotations are scored according to certain attributes, including study size, reported P value, and whether the variant annotation supports or fails to find an association. Clinical guidelines or US Food and Drug Administration (FDA)-approved drug labels which give variant-specific prescribing guidance are also scored. The scores of all annotations attached to a clinical annotation are summed together to give a total score for the clinical annotation, which is used to calculate an LOE. Overall, the system increases transparency, consistency, and reproducibility in LOE assignment to clinical annotations. In combination with increased standardization of how clinical annotations are written, use of this scoring system helps to ensure that PharmGKB clinical annotations continue to be a robust source of pharmacogenomic information.
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Project description:While there is no consensus about the definition of complexity, it is widely accepted that the ability to produce uncertainty is the most prominent characteristic of complex systems. We introduce new metrics that purport to quantify the complexity of living organisms and social organizations based on their levels of uncertainty. We consider three major dimensions regarding complexity: diversity based on the number of system elements and the number of categories of these elements; flexibility which bears upon variations in the elements; and combinability which refers to the patterns of connection between elements. These three dimensions are quantified using Shannon's uncertainty formula, and they can be integrated to provide a tripartite complexity index. We provide a calculation example that illustrates the use of these indices for comparing the complexity of different social systems. These indices distinguish themselves by a theoretical basis grounded on the amount of uncertainty, and the requirement that several aspects of the systems be accounted for to compare their degree of complexity. We expect that these new complexity indices will encourage research programmes aiming to compare the complexity levels of systems belonging to different realms.
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Project description:Effective implementation of precision medicine will be enhanced by a thorough understanding of each patient's genetic composition to better treat his or her presenting symptoms or mitigate the onset of disease. This ideally includes the sequence information of a complete genome for each individual. At Partners HealthCare Personalized Medicine, we have developed a clinical process for whole genome sequencing (WGS) with application in both healthy individuals and those with disease. In this manuscript, we will describe our bioinformatics strategy to efficiently process and deliver genomic data to geneticists for clinical interpretation. We describe the handling of data from FASTQ to the final variant list for clinical review for the final report. We will also discuss our methodology for validating this workflow and the cost implications of running WGS.
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Project description:BackgroundThe need for healthcare curricula renewal to facilitate a continuum in education from classrooms to diverse healthcare settings is undeniable. Simulation has been recognized as an educational strategy to address healthcare education challenges, with limited reporting on the integration of simulation-based learning experiences in physiotherapy education. The study aimed to describe the finalisation of a framework for integration of healthcare simulation in an undergraduate physiotherapy program.MethodsA qualitative descriptive research design was utilized. Five South African experts in the fields of healthcare simulation and/or physiotherapy education contributed to the finalization of the framework during a consensus meeting. Content analysis was employed and credibility was ensured through double coding.ResultsStructural coding yielded five themes- Planning, Implementation, Program Evaluation, Program Revision and Framework. The five themes consisted of fifteen categories, two sub-categories and 44 codes. The planning theme was most robust with seven categories. The Planning, Implementation, Evaluation, Revision (PIER) framework was developed and finalized by expert participants. following the consensus meeting.ConclusionNeeds analyses when planning and incorporating simulation is essential. Collaboration through resource and knowledge sharing is vital in developing a responsive curriculum integrating simulation. Furthermore, facilitator and student preparation are paramount in ensuring active engagement in simulated-based learning experiences. The interconnectedness of all framework elements and integration phases, as well as the implied importance of competent facilitators and prepared students is crucial and highlights careful consideration to be given to these aspects. The PIER framework is generic in nature and represents the continuous process of simulation integration for any healthcare program.
| S-EPMC9509622 | biostudies-literature
Project description:Regenerative medicine has the potential to drastically change the field of health care from reactive to preventative and restorative. Exciting advances in stem cell biology and cellular reprogramming have fueled the progress of this field. Biochemical cues in the form of small molecule drugs, growth factors, zinc finger protein transcription factors and nucleases, transcription activator-like effector nucleases, monoclonal antibodies, plasmid DNA, aptamers, or RNA interference agents can play an important role to influence stem cell differentiation and the outcome of tissue regeneration. Many of these biochemical factors are fragile and must act intracellularly at the molecular level. They require an effective delivery system, which can take the form of a scaffold (e.g., hydrogels and electrospun fibers), carrier (viral and nonviral), nano- and microparticle, or genetically modified cell. In this review, we will discuss the history and current technologies of drug, protein, and gene delivery in the context of regenerative medicine. Next, we will present case examples of how delivery technologies are being applied to promote angiogenesis in nonhealing wounds or prevent angiogenesis in age related macular degeneration. Finally, we will conclude with a brief discussion of the regulatory pathway from bench to bedside for the clinical translation of these novel therapeutics.
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Project description:BackgroundThe goal of personalized medicine is to provide patients optimal drug screening and treatment based on individual genomic or proteomic profiles. Reverse-Phase Protein Array (RPPA) technology offers proteomic information of cancer patients which may be directly related to drug sensitivity. For cancer patients with different drug sensitivity, the proteomic profiling reveals important pathophysiologic information which can be used to predict chemotherapy responses.ResultsThe goal of this paper is to present a framework for personalized medicine using both RPPA and drug sensitivity (drug resistance or intolerance). In the proposed personalized medicine system, the prediction of drug sensitivity is obtained by a proposed augmented naive Bayesian classifier (ANBC) whose edges between attributes are augmented in the network structure of naive Bayesian classifier. For discriminative structure learning of ANBC, local classification rate (LCR) is used to score augmented edges, and greedy search algorithm is used to find the discriminative structure that maximizes classification rate (CR). Once a classifier is trained by RPPA and drug sensitivity using cancer patient samples, the classifier is able to predict the drug sensitivity given RPPA information from a patient.ConclusionIn this paper we proposed a framework for personalized medicine where a patient is profiled by RPPA and drug sensitivity is predicted by ANBC and LCR. Experimental results with lung cancer data demonstrate that RPPA can be used to profile patients for drug sensitivity prediction by Bayesian network classifier, and the proposed ANBC for personalized cancer medicine achieves better prediction accuracy than naive Bayes classifier in small sample size data on average and outperforms other the state-of-the-art classifier methods in terms of classification accuracy.
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Project description:BackgroundThe combination of imaging and therapeutic agents in the same smart nanoparticle is a promising option to perform a minimally invasive imaging guided therapy. In this study, Low density lipoproteins (LDL), one of the most attractive biodegradable and biocompatible nanoparticles, were used for the simultaneous delivery of Paclitaxel (PTX), a hydrophobic antitumour drug and an amphiphilic contrast agent, Gd-AAZTA-C17, in B16-F10 melanoma cell line. These cells overexpress LDL receptors, as assessed by flow cytometry analysis.ResultsPTX and Gd-AAZTA-C17 loaded LDLs (LDL-PTX-Gd) have been prepared, characterized and their stability was assessed under 72 h incubation at 37 °C and compared to LDL loaded with Gd-AAZTA-C17 (LDL-Gd) and LDL-PTX. The cytotoxic effect of LDL-PTX-Gd was evaluated by MTT assay. The anti-tumour drug loaded into LDLs showed a significantly higher toxicity on B16-F10 cells with respect to the commercially available formulation Paclitaxel kabi (PTX Kabi) used in clinical applications. Tumour cells uptake was initially assessed by ICP-MS and MRI on B16-F10 cell line. By the analysis of the image signal intensity, it was possible to extrapolate the amount of internalized PTX indirectly by the decrease of relaxation times caused by Gd, proportional to its concentration. Finally, the treatment with PTX loaded LDL on B16-F10 tumour bearing mice resulted in a marked reduction of tumour growth compared to the administration of PTX Kabi alone.ConclusionsLDLs are selectively taken-up by tumour cells and can be successfully exploited for the selective delivery of Paclitaxel and imaging agents. For the first time the anon invasive "in vivo" determination of the amount of PTX accumulated in the tumour was possible, thanks to the use of theranostic agents of natural origin.
| S-EPMC8276427 | biostudies-literature