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

Artificial Pancreas: the Argentine experience

The objective of this work is to present a brief review on the international Artificial Pancreas project. In addition, the local project that led to the first Latin American clinical trials with an Artificial Pancreas will be described. These trials were performed in Buenos Aires during 2016 and 2017. The last trial used an algorithm developed in Argentina and defined as the ARG (Automatic Regulation of Glucose). This procedure and its in silico and clinical results will also be presented in this paper.Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señale

Feasibility Design of a Continuous Insulin Sensor from Lessons Learned using Glucose Sensors, and Point of Care Insulin Sensors

abstract: Glucose sensors have had many paradigm shifts, beginning with using urine, to point of care blood, now being approved for implant. This review covers various aspects of the sensors, ranging from the types of surface chemistry, and electron transduction. All the way to the algorithms, and filters used to alter and understand the signal being transduced. Focus is given to Dr. Hellerâ’s work using redox mediators, as well as Dr. Sode in his advances for direct electron transfer. Simple process of designing sensors are described, as well as the possible errors that may come with glucose sensor use. Finally, a small window into the future trends of glucose sensors is described both from a device view point, as well as organic viewpoint. Using this history the initial point of care sensor for insulin published through LaBelle’s lab is reevaluated critically. In addition, the modeling of the possibility of continuously measuring insulin is researched. To better understand the design for a continuous glucose sensor, the basic kinetic model is set up, and ran through a design of experiments to then optimized what the binding kinetics for an ideal insulin molecular recognition element would be. In addition, the phenomena of two electrochemical impedance spectroscopy peaks is analyzed, and two theories are suggests, and demonstrated to a modest level.Dissertation/ThesisMasters Thesis Biomedical Engineering 201

Integrating Meal and Exercise into Personalized Glucoregulation Models: Metabolic Dynamics and Diabetic Athletes

Diabetes affects nearly 26 million Americans, according to the American Diabetes Association, with as many as three million Americans who have Type 1 Diabetes (ADA, 2015). Type 1 Diabetes (T1D) is autoimmune and characterized by little to no insulin production whereas Type 2 Diabetes (T2D) concerns insulin resistance and inability to use produced insulin. Factors contributing to current diabetes management and regulation include exercise type, daily movement activities, and distinct tissue compartment metabolism, each challenging to model in a robust and comprehensive manner. Past models are highly limited in regard to exercise and varying glucose fluctuations dependent on type, intensity, and duration. Modeling could greatly enhance factors that contribute to diabetes management—currently, T1D is managed with a pump and/or injections, informed by constant blood glucose monitoring. This thesis addresses knowledge gaps in the management and etiology of diabetes through development of a novel dynamic mathematical model informing controller design and implementation (artificial pancreas, continuous glucose monitors, and pumps). Diet and meal content on the basis of varying glycemic index and on the effects of activity and exercise, with lifestyle habit implications are a main focus. Emphasis is placed on model personalization with a T1D athlete example. The following model and case study implement specific aims: • 10th order model designed in Matlab with 4 interrelated sub-models to integrate meal diversity, exercise activities, and personalized body composition. o 3-State Glucose Compartmental Model o 2-State Control Mechanisms: Insulin and Glucagon o 2-State Digestion Model o 2-State Exogenous Insulin Control o Skeletal Muscle Model with Mitochondrial State o Nonlinear relations including Hill Functions • A 2 Phase Case Study, IRB approved for a Type 1 athletic 23-year-old female to evaluate and develop the model. Results illustrate effects of meal type (slow vs. fast glycemic index) and exercise/activity based glucose-glycogen consumption on blood plasma glucose predictions and hormonal control action for both non-diabetic and diabetic model versions. Current challenges are addressed with model personalization, providing input flexibility for body mass, muscle ratio, stress, and types of diabetes (T1D, T2D) informing artificial pancreas design and possible sports performance applications

Optimal Control Strategies for Complex Biological Systems

To better understand and to improve therapies for complex diseases such as cancer or diabetes, it is not sufficient to identify and characterize the interactions between molecules and pathways in complex biological systems, such as cells, tissues, and the human body. It also is necessary to characterize the response of a biological system to externally supplied agents (e.g., drugs, insulin), including a proper scheduling of these drugs, and drug combinations in multi drugs therapies. This obviously becomes important in applications which involve control of physiological processes, such as controlling the number of autophagosome vesicles in a cell, or regulating the blood glucose level in patients affected by diabetes. A critical consideration when controlling physiological processes in biological systems is to reduce the amount of drugs used, as in some therapies drugs may become toxic when they are overused. All of the above aspects can be addressed by using tools provided by the theory of optimal control, where the externally supplied drugs or hormones are the inputs to the system. Another important aspect of using optimal control theory in biological systems is to identify the drug or the combination of drugs that are effective in regulating a given therapeutic target, i.e., a biological target of the externally supplied stimuli. The dynamics of the key features of a biological system can be modeled and described as a set of nonlinear differential equations. For the implementation of optimal control theory in complex biological systems, in what follows we extract \textit{a network} from the dynamics. Namely, to each state variable $x_i$ we will assign a network node $v_i$ ($i=1,...,N$) and a network directed edge from node $v_i$ to another node $v_j$ will be assigned every time $x_j$ is present in the time derivative of $x_i$. The node which directly receives an external stimulus is called a \emph{driver nodes} in a network. The node which directly connected to an output sensor is called a \emph{target node}. %, and it has a prescribed final state that we wish to achieve in finite time. From the control point of view, the idea of controllability of a system describes the ability to steer the system in a certain time interval towards thea desired state with a suitable choice of control inputs. However, defining controllability of large complex networks is quite challenging, primarily because of the large size of the network, its complex structure, and poor knowledge of the precise network dynamics. A network can be controllable in theory but not in practice when a very large control effort is required to steer the system in the desired direction. This thesis considers several approaches to address some of these challenges. Our first approach is to reduce the control effort is to reduce the number of target nodes. We see that by controlling the states of a subset of the network nodes, rather than the state of every node, while holding the number of control signals constant, the required energy to control a portion of the network can be reduced substantially. The energy requirements exponentially decay with the number of target nodes, suggesting that large networks can be controlled by a relatively small number of inputs as long as the target set is appropriately sized. We call this strategy \emph{target control}. As our second approach is based on reducing the control efforts by allowing the prescribed final states are satisfied approximately rather than strictly. We introduce a new control strategy called \textit{balanced control} for which we set our objective function as a convex combination of two competitive terms: (i) the distance between the output final states at a given final time and given prescribed states and (ii) the total control efforts expenditure over the given time period. Based on the above two approaches, we propose an algorithm which provides a locally optimal control technique for a network with nonlinear dynamics. We also apply pseudo-spectral optimal control, together with the target and balance control strategies previously described, to complex networks with nonlinear dynamics. These optimal control techniques empower us to implement the theoretical control techniques to biological systems evolving with very large, complex and nonlinear dynamics. We use these techniques to derive the optimal amounts of several drugs in a combination and their optimal dosages. First, we provide a prediction of optimal drug schedules and combined drug therapies for controlling the cell signaling network that regulates autophagy in a cell. Second, we compute an optimal dual drug therapy based on administration of both insulin and glucagon to control the blood glucose level in type I diabetes. Finally, we also implement the combined control strategies to investigate the emergence of cascading failures in the power grid networks

Report from IPITA-TTS opinion leaders meeting on the future of β-cell replacement

Peer reviewe

REGULATION OF BLOOD GLUCOSE IN TYPE I DIABETIC PATIENTS

Ph.DDOCTOR OF PHILOSOPH

Microfluidic Organ/Body-on-a-Chip Devices at the Convergence of Biology and Microengineering

Recent advances in biomedical technologies are mostly related to the convergence of biology with microengineering. For instance, microfluidic devices are now commonly found in most research centers, clinics and hospitals, contributing to more accurate studies and therapies as powerful tools for drug delivery, monitoring of specific analytes, and medical diagnostics. Most remarkably, integration of cellularized constructs within microengineered platforms has enabled the recapitulation of the physiological and pathological conditions of complex tissues and organs. The so-called organ-on-a-chip technology, which represents a new avenue in the field of advanced in vitro models, with the potential to revolutionize current approaches to drug screening and toxicology studies. This review aims to highlight recent advances of microfluidic-based devices towards a body-on-a-chip concept, exploring their technology and broad applications in the biomedical field.European Regional Development Fund-Project FNUSA-ICRC [CZ.1.05/1.1.00/02.0123]; Fundacao para a Ciencia e a Tecnologia (FCT), Portugal [UID/BIM/04773/2013]; Internal Research Grant Program, Universita Campus Bio-Medico di Romainfo:eu-repo/semantics/publishedVersio

Regional Intestinal Drug Absorption

The gastrointestinal tract (GIT) can be broadly divided into several regions: the stomach, the small intestine (which is subdivided to duodenum, jejunum, and ileum), and the colon. The conditions and environment in each of these segments, and even within the segment, are dependent on many factors, e.g., the surrounding pH, fluid composition, transporters expression, metabolic enzymes activity, tight junction resistance, different morphology along the GIT, variable intestinal mucosal cell differentiation, changes in drug concentration (in cases of carrier-mediated transport), thickness and types of mucus, and resident microflora. Each of these variables, alone or in combination with others, can fundamentally alter the solubility/dissolution, the intestinal permeability, and the overall absorption of various drugs. This is the underlying mechanistic basis of regional-dependent intestinal drug absorption, which has led to many attempts to deliver drugs to specific regions throughout the GIT, aiming to optimize drug absorption, bioavailability, pharmacokinetics, and/or pharmacodynamics. In the book "Regional Intestinal Drug Absorption: Biopharmaceutics and Drug Formulation" we aim to highlight the current progress and to provide an overview of the latest developments in the field of regional-dependent intestinal drug absorption and delivery, as well as pointing out the unmet needs of the field