In silico assessment of biomedical products: the conundrum of rare but not so rare events in two case studies

In silico clinical trials, defined as “The use of individualized computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention,” have been proposed as a possible strategy to reduce the regulatory costs of innovation and the time to market for biomedical products. We review some of the the literature on this topic, focusing in particular on those applications where the current practice is recognized as inadequate, as for example, the detection of unexpected severe adverse events too rare to be detected in a clinical trial, but still likely enough to be of concern. We then describe with more details two case studies, two successful applications of in silico clinical trial approaches, one relative to the University of Virginia/Padova simulator that the Food and Drug Administration has accepted as possible replacement for animal testing in the preclinical assessment of artificial pancreas technologies, and the second, an investigation of the probability of cardiac lead fracture, where a Bayesian network was used to combine in vivo and in silico observations, suggesting a whole new strategy of in silico-augmented clinical trials, to be used to increase the numerosity where recruitment is impossible, or to explore patients’ phenotypes that are unlikely to appear in the trial cohort, but are still frequent enough to be of concern

Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis

One of the main challenges of glucose control in patients with type 1 diabetes is identifying a control-oriented model that reliably predicts the behavior of glycemia. Here, a review is provided emphasizing the structural identifiability and observability properties, which surprisingly reveals that few of them are globally identifiable and observable at the same time. Thus, a general proposal was developed to encompass four linear models according to suitable assumptions and transformations. After the corresponding structural properties analysis, two minimal model structures are generated, which are globally identifiable and observable. Then, the practical identifiability is analyzed for this application showing that the standard collected data in many cases do not have the necessary quality to ensure a unique solution in the identification process even when a considerable amount of data is collected. The two minimal control-oriented models were identified using a standard identification procedure using data from 30 virtual patients of the UVA/Padova simulator and 77 diabetes care data from adult patients of a diabetes center. The identification was performed in two stages: calibration and validation. In the first stage, the average length was taken as two days (dictated by the practical identifiability). For both structures, the mean absolute error was 16.8 mg/dl and 9.9 mg/dl for virtual patients and 21.6 mg/dl and 21.5 mg/dl for real patients. For the second stage, a one-day validation window was considered long enough for future artificial pancreas applications. The mean absolute error was 23.9 mg/dl and 12.3 mg/dl for virtual patients and 39.2 mg/dl and 36.6 mg/dl for virtual and real patients. These results confirm that linear models can be used as prediction models in model-based control strategies as predictive control.Fil: Hoyos, J. D.. Universidad Nacional de Colombia. Sede Medellín; ColombiaFil: Villa Tamayo, M. F.. Universidad Nacional de Colombia. Sede Medellín; ColombiaFil: Builes Montano, C. E.. Universidad de Antioquia; ColombiaFil: Ramirez Rincon, A.. Universidad Pontificia Bolivariana; ColombiaFil: Godoy, José Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Garcia Tirado, J.. University of Virginia; Estados UnidosFil: Rivadeneira Paz, Pablo Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentin

Control-Oriented Model With Intra-Patient Variations for an Artificial Pancreas

In this work, a low-order model designed for glucose regulation in Type 1 Diabetes Mellitus (T1DM) is obtained from the UVA/Padova metabolic simulator. It captures not only the nonlinear behavior of the glucose-insulin system, but also intra-patient variations related to daily insulin sensitivity (SI) changes. To overcome the large inter-subject variability, the model can also be personalized based on a priori patient information. The structure is amenable for linear parameter varying (LPV) controller design, and represents the dynamics from the subcutaneous insulin input to the subcutaneous glucose output. The efficacy of this model is evaluated in comparison with a previous control-orientedmodel which in turn is an improvement of previous models. Both models are compared in terms of their open- and closed-loop differences with respect to the UVA/Padova model. The proposed model outperforms previous T1DM control-oriented models, which could potentially lead to more robust and reliable controllers for glycemia regulation.Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señale

Gramáticas evolutivas para la predicción de hipoglucemias en diabetes

Trabajo de Fin de Grado en Ingeniería Informática, Facultad de Informática UCM, Departamento Ingeniería del Software e Inteligencia Artificial, Curso 2021/2022. https://github.com/ABSysGroup/jeco/tree/TFG_GE4HYPODiabetic patients have to manage their blood sugar correctly to prevent complications. One such complication is hypoglycemia or low blood sugar, which occurs when the blood glucose concentration goes below a certain threshold. A hypoglycemic episode needs to be rectified before it becomes harmful and can be a very distressing situation for the patient. The main goal of this study is to program Structured Grammatical Evolution and Dynamic Structured Grammatical Evolution algorithms and use them to generate models for the prediction of hypoglycemic episodes in patients with diabetes. The algorithms will be used to obtain a white-box model made up of if-then-else statements that given some input data, comprised of the blood glucose and exercise readings of the patients from the previous 2 hours, optimizes a logical relation between these variables. The resulting formula will be able to determine if the patient is going to have a hypoglycemic episode in a 30, 60, 90 and 120 minutes prediction horizon.Los pacientes con diabetes deben controlar correctamente su nivel de azúcar en sangre para evitar complicaciones. Una de estas complicaciones es la hipoglucemia o nivel bajo de azúcar en sangre, que ocurre cuando la concentración de glucosa en la sangre cae por debajo de cierto umbral. Un episodio de hipoglucemia debe corregirse antes de que se vuelva dañino y puede ser una situación muy angustiosa para el paciente. El objetivo principal de este estudio es programar los algoritmos de Gramáticas Evolutivas Estructuradas y Gramáticas Evolutivas Estructuradas Dinámicas, y utilizarlos para generar modelos de predicción de episodios hipoglucémicos en pacientes con diabetes. Los algoritmos se utilizarán para obtener un modelo de caja blanca formado por sentencias if-then-else que, dados unos datos de entrada, compuestos por el nivel de la glucosa en sangre y las lecturas de datos de ejercicio de los pacientes de las 2 horas anteriores, optimiza una relación lógica entre estas variables. La fórmula resultante se usa para determinar si el paciente va a tener un episodio de hipoglucemia en un plazo de 30, 60, 90 y 120 minutos.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

PID PER IL CONTROLLO GLICEMICO NEL PANCREAS ARTIFICIALE E NEL CLAMP GLICEMICO

Background e Obbiettivi: questo progetto di tesi si compone di due parti, dove queste hanno in comune l'utilizzo del controllore PID per compiere un determinato scopo. La prima parte studia l'utilizzo del PID nel Pancreas Artificiale mentre nella seconda parte viene studiato il PID nel Clamp Glicemico. Il Pancreas Artificiale (AP) è uno strumento utilizzato nella terapia del diabete mellito di tipo 1 che cerca di dare al paziente una “nuova vita” priva di preoccupazioni riguardanti gli andamenti glicemici, mentre il Clamp Glicemico è una tecnica sperimentale utilizzata nella valutazione di diversi aspetti del metabolismo del glucosio. In ambedue i casi si affronta la sfida non banale di regolare accuratamente i parametri del PID, nel primo caso si cerca di modellare la velocità di infusione di insulina per portare la glicemia del paziente in un range di valori il più fisiologici possibile, mentre nel secondo caso si cerca di modellare la velocità di infusione endovenosa di glucosio esterna per portare la concentrazione di glucosio nel sangue (BG) dei soggetti verso un livello di plateau desiderato. Metodi: nel primo studio si sviluppa il PID con azione di feedforward, simulando così un AP totale, e senza azione di feedforward, replicando così un AP ibrido. Nella seconda parte del lavoro si utilizza il medesimo concetto di PID ma nel contesto del Clamp Glicemico per tre tipi diversi di scenario: iperglicemico, euglicemico e ipoglicemico. In ambedue i casi studio, viene svolto il processo di convalida attraverso un simulatore che emula le variabilità glicemiche di 10 pazienti virtuali (il medesimo del caso precedente), in più include anche modelli di errore di misurazione e ritardo di campionamento per un maggiore realismo. Risultati: Nell’AP il PID ha dimostrato una buona capacità nello portare la glicemia dei pazienti secondo il livello di riferimento fornito. Nel caso dell’AP totale la mancanza dell’azione di feedforward non aiuta a prevenire l’innalzamento glicemico dovuto all’assunzione di carboidrati, ma riesce ad ogni modo a portare in tempi brevi la glicemia a livello di riferimento, nel caso invece dell’AP ibrido il picco glicemico si abbassa notevolmente e porta la glicemica del paziente a riferimento in breve tempo. Nel Clamp Glicemico il PID riesce a compiere il suo dovere in tutti e tre i casi: Iperglicemico, Euglicemico ed Iperglicemico mantenendo fissa la glicemia. Conclusioni: I risultati ottenuti dal PID sia nell’ambito dell’AP sia nel Clamp Glicemico danno evidenti segni che la strada percorsa è corretta e può continuare in un continuo sviluppo e miglioramento di questi dispositivi nella sicurezza ed efficacia in vivo. Portando così ad una qualità di vita migliore per il paziente diabetico, più semplice e meno rischiosa e, migliorando la qualità degli esperimenti con il clamp di glucosio