FARMAPRICE: A Pharmacogenetic Clinical decision support system for precise and Cost-Effective Therapy

Pharmacogenetic (PGx) guidelines for the precise dosing and selection of drugs remain poorly implemented in current clinical practice. Among the barriers to the implementation process is the lack of clinical decision support system (CDSS) tools to aid health providers in managing PGx information in the clinical context. The present study aimed to describe the first Italian endeavor to develop a PGx CDSS, called FARMAPRICE. FARMAPRICE prototype was conceived for integration of patient molecular data into the clinical prescription process in the Italian Centro di Riferimento Oncologico (CRO)-Aviano Hospital. It was developed through a coordinated partnership between two high-tech companies active in the computerization of the Italian healthcare system. Introducing FARMAPRICE into the clinical setting can aid physicians in prescribing the most efficacious and cost-effective pharmacological therapy available

Feasibility of incorporating genomic knowledge into electronic medical records for pharmacogenomic clinical decision support

In pursuing personalized medicine, pharmacogenomic (PGx) knowledge may help guide prescribing drugs based on a person’s genotype. Here we evaluate the feasibility of incorporating PGx knowledge, combined with clinical data, to support clinical decision-making by: 1) analyzing clinically relevant knowledge contained in PGx knowledge resources; 2) evaluating the feasibility of a rule-based framework to support formal representation of clinically relevant knowledge contained in PGx knowledge resources; and, 3) evaluating the ability of an electronic medical record/electronic health record (EMR/EHR) to provide computable forms of clinical data needed for PGx clinical decision support. Findings suggest that the PharmGKB is a good source for PGx knowledge to supplement information contained in FDA approved drug labels. Furthermore, we found that with supporting knowledge (e.g. IF age <18 THEN patient is a child), sufficient clinical data exists in University of Washington’s EMR systems to support 50% of PGx knowledge contained in drug labels that could be expressed as rules

Doctor of Philosophy

dissertationThe widespread use of genomic information to improve clinical care has long been a goal of clinicians, researchers, and policy-makers. With the completion of the Human Genome Project over a decade ago, the feasibility of attaining this goal on a widespread basis is becoming a greater reality. In fact, new genome sequencing technologies are bringing the cost of obtaining a patient's genomic information within reach of the general population. While this is an exciting prospect to health care, many barriers still remain to effectively use genomic information in a clinically meaningful way. These barriers, if not overcome, will limit the ability of genomic information to provide a significant impact on health care. Nevertheless, clinical decision support (CDS), which entails the provision of patient-specific knowledge to clinicians at appropriate times to enhance health care, offers a feasible solution. As such, this body of work represents an effort to develop a functional CDS solution capable of leveraging whole genome sequence information on a widespread basis. Many considerations were made in the design of the CDS solution in order to overcome the complexities of genomic information while aligning with common health information technology approaches and standards. This work represents an important advancement in the capabilities of integrating actionable genomic information within the clinical workflow using health informatics approaches

Propagation of Pericentral Necrosis During Acetaminophen-Induced Liver Injury: Evidence for Early Interhepatocyte Communication and Information Exchange.

Acetaminophen (APAP)-induced liver injury is clinically significant, and APAP overdose in mice often serves as a model for drug-induced liver injury in humans. By specifying that APAP metabolism, reactive metabolite formation, glutathione depletion, and mitigation of mitochondrial damage within individual hepatocytes are functions of intralobular location, an earlier virtual model mechanism provided the first concrete multiattribute explanation for how and why early necrosis occurs close to the central vein (CV). However, two characteristic features could not be simulated consistently: necrosis occurring first adjacent to the CV, and subsequent necrosis occurring primarily adjacent to hepatocytes that have already initiated necrosis. We sought parsimonious model mechanism enhancements that would manage spatiotemporal heterogeneity sufficiently to enable meeting two new target attributes and conducted virtual experiments to explore different ideas for model mechanism improvement at intrahepatocyte and multihepatocyte levels. For the latter, evidence supports intercellular communication via exosomes, gap junctions, and connexin hemichannels playing essential roles in the toxic effects of chemicals, including facilitating or counteracting cell death processes. Logic requiring hepatocytes to obtain current information about whether downstream and lateral neighbors have triggered necrosis enabled virtual hepatocytes to achieve both new target attributes. A virtual hepatocyte that is glutathione-depleted uses that information to determine if it will initiate necrosis. When a less-stressed hepatocyte is flanked by at least two neighbors that have triggered necrosis, it too will initiate necrosis. We hypothesize that the resulting intercellular communication-enabled model mechanism is analogous to the actual explanation for APAP-induced hepatotoxicity at comparable levels of granularity

Integration of modeling and simulation into hospital-based decision support systems guiding pediatric pharmacotherapy

<p>Abstract</p> <p>Background</p> <p>Decision analysis in hospital-based settings is becoming more common place. The application of modeling and simulation approaches has likewise become more prevalent in order to support decision analytics. With respect to clinical decision making at the level of the patient, modeling and simulation approaches have been used to study and forecast treatment options, examine and rate caregiver performance and assign resources (staffing, beds, patient throughput). There us a great need to facilitate pharmacotherapeutic decision making in pediatrics given the often limited data available to guide dosing and manage patient response. We have employed nonlinear mixed effect models and Bayesian forecasting algorithms coupled with data summary and visualization tools to create drug-specific decision support systems that utilize individualized patient data from our electronic medical records systems.</p> <p>Methods</p> <p>Pharmacokinetic and pharmacodynamic nonlinear mixed-effect models of specific drugs are generated based on historical data in relevant pediatric populations or from adults when no pediatric data is available. These models are re-executed with individual patient data allowing for patient-specific guidance via a Bayesian forecasting approach. The models are called and executed in an interactive manner through our web-based dashboard environment which interfaces to the hospital's electronic medical records system.</p> <p>Results</p> <p>The methotrexate dashboard utilizes a two-compartment, population-based, PK mixed-effect model to project patient response to specific dosing events. Projected plasma concentrations are viewable against protocol-specific nomograms to provide dosing guidance for potential rescue therapy with leucovorin. These data are also viewable against common biomarkers used to assess patient safety (e.g., vital signs and plasma creatinine levels). As additional data become available via therapeutic drug monitoring, the model is re-executed and projections are revised.</p> <p>Conclusion</p> <p>The management of pediatric pharmacotherapy can be greatly enhanced via the immediate feedback provided by decision analytics which incorporate the current, best-available knowledge pertaining to dose-exposure and exposure-response relationships, especially for narrow therapeutic agents that are difficult to manage.</p

Approximate dynamic programming for anemia management.

The focus of this dissertation work is the formulation and improvement of anemia management process involving trial-and-error. A two-stage method is adopted toward this objective. Given a medical treatment process, a discrete Markov representation is first derived as a formal translation of the treatment process to a control problem under uncertainty. A simulative numerical solution of the control problem is then obtained on-the-fly in the form of a control law maximizing the long-term benefit at each decision stage. Approximate dynamic programming methods are employed in the proposed solution. The motivation underlying this choice is that, in reality, some patient characteristics, which are critical for the sake of treatment, cannot be determined through diagnosis and remain unknown until early stages of treatment, when the patient demonstrates them upon actions by the decision maker. A review of these simulative control tools, which are studied extensively in reinforcement learning theory, is presented. Two approximate dynamic programming tools, namely SARSA and Q -learning, are introduced. Their performance in discovering the optimal individualized drug dosing policy is illustrated on hypothetical patients made up as fuzzy models for simulations. As an addition to these generic reinforcement learning methods, a state abstraction scheme for the considered application domain is also proposed. The control methods of this study, capturing the essentials of a drug delivery problem, constitutes a novel computational framework for model-free medical treatment. Experimental evaluation of the dosing strategies produced by the proposed methods against the standard policy, which is being followed actually by human experts in Kidney Diseases Program, University of Louisville, shows the advantages for use of reinforcement learning in the drug dosing problem in particular and in medical decision making in general

Diagnostic and medical needs for therapeutic drug monitoring of antibiotics

Therapeutic drug monitoring (TDM) of antibiotics has been practiced for more than half a century, but it is still not widely applied for infected patients. It has a traditional focus on limiting toxicity of specific classes of antibiotics such as aminoglycosides and vancomycin. With more patients in critical care with higher levels of sickness severity and immunosuppression as well as an increasingly obese and ageing population, an increasing risk of suboptimal antibiotic exposure continues to escalate. As such, the value of TDM continues to expand, especially for beta-lactams which constitute the most frequently used antibiotic class. To date, the minimum inhibitory concentration (MIC) of infectious microbes rather than classification in terms of susceptible and resistant can be reported. In parallel, increasingly sophisticated TDM technology is becoming available ensuring that TDM is feasible and can deliver personalized antibiotic dosing schemes. There is an obvious need for extensive studies that will quantify the improvements in clinical outcome of individual TDM-guided dosing. We suggest that a broad diagnostic and medical investigation of the TDM arena, including marke

Network and systems medicine: Position paper of the European Collaboration on Science and Technology action on Open Multiscale Systems Medicine

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management