Advanced multiparametric optimization and control studies for anaesthesia

Anaesthesia is a reversible pharmacological state of the patient where hypnosis, analgesia and muscle relaxation are guaranteed and maintained throughout the surgery. Analgesics block the sensation of pain; hypnotics produce unconsciousness, while muscle relaxants prevent unwanted movement of muscle tone. Controlling the depth of anaesthesia is a very challenging task, as one has to deal with nonlinearity, inter- and intra-patient variability, multivariable characteristics, variable time delays, dynamics dependent on the hypnotic agent, model analysis variability, agent and stability issues. The modelling and automatic control of anaesthesia is believed to (i) benefit the safety of the patient undergoing surgery as side-effects may be reduced by optimizing the drug infusion rates, and (ii) support anaesthetists during critical situations by automating the drug delivery systems. In this work we have developed several advanced explicit/multi-parametric model predictive (mp-MPC) control strategies for the control of depth of anaesthesia. State estimation techniques are developed and used simultaneously with mp-MPC strategies to estimate the state of each individual patient, in an attempt to overcome the challenges of inter- and intra- patient variability, and deal with possible unmeasurable noisy outputs. Strategies to deal with the nonlinearity have been also developed including local linearization, exact linearization as well as a piece-wise linearization of the Hill curve leading to a hybrid formulation of the patient model and thereby the development of multiparametric hybrid model predictive control methodology. To deal with the inter- and intra- patient variability, as well as the noise on the process output, several robust techniques and a multiparametric moving horizon estimation technique have been design and implemented. All the studies described in the thesis are performed on clinical data for a set of 12 patients who underwent general anaesthesia.Open Acces

Reconfigurable Filtering of Neuro-Spike Communications Using Synthetically Engineered Logic Circuits.

High-frequency firing activity can be induced either naturally in a healthy brain as a result of the processing of sensory stimuli or as an uncontrolled synchronous activity characterizing epileptic seizures. As part of this work, we investigate how logic circuits that are engineered in neurons can be used to design spike filters, attenuating high-frequency activity in a neuronal network that can be used to minimize the effects of neurodegenerative disorders such as epilepsy. We propose a reconfigurable filter design built from small neuronal networks that behave as digital logic circuits. We developed a mathematical framework to obtain a transfer function derived from a linearization process of the Hodgkin-Huxley model. Our results suggest that individual gates working as the output of the logic circuits can be used as a reconfigurable filtering technique. Also, as part of the analysis, the analytical model showed similar levels of attenuation in the frequency domain when compared to computational simulations by fine-tuning the synaptic weight. The proposed approach can potentially lead to precise and tunable treatments for neurological conditions that are inspired by communication theory

Preclinical pharmacokinetic evaluation of novel antimalarial and antituberculosis drug leads

Preclinical pharmacokinetics relies on efficient and accurate screening to select clinical candidates from early leads. Poor pharmacokinetic interpretation can disadvantage drug discovery by promoting inadequate compounds and expelling potential drug candidates. Objectives of this project included pharmacokinetic evaluation of antimalarial and anti-tuberculosis lead compounds with techniques aimed at improving preclinical pharmacokinetic outcomes. This included mechanistic pharmacokinetic approaches such as non-linear mixed effects (NLME) modelling in comparison with traditional non-compartmental analysis. Where appropriate, pharmacokinetic methods were expanded to include organ distribution and capsule dosing in mice to bridge our techniques from discovery to early development. Three benzoxazole amodiaquine analogues possessing equipotent in vitro antiplasmodial activity and showed diverse in vivo efficacy in a malaria mouse model. Evaluation of their respective pharmacokinetics in mice showed their in vivo exposures could translate to in vivo efficacy. Retrospective PK/PD simulations point to a time above IC50 drive in efficacy. Pharmacokinetic evaluation of an aminopyridine antimalarial compound in its cyclodextrin inclusion complex revealed a pH dependent increase in solubility that reduced variance, likely due to favoured intestinal absorption. Investigation of two novel fusidic acid C-3 ester prodrugs aimed at repositioning fusidic acid for tuberculosis, showed high concentrations of the rodent specific 3-epifusidic acid metabolite that greatly reduced exposure of fusidic acid in mice. Further organ distribution studies showed a prodrug strategy is still viable for repositioning fusidic acid for tuberculosis, but that rodent models are inappropriate for further evaluation. NLME modelling successfully provided unique mechanistic and mathematical insight of pharmacokinetic profiles of new leads. The level of interpretation on pharmacology parameters improved and aided in understanding why drug leads are likely to fail or succeed, assisting future compound optimisation

On Automation in Anesthesia

The thesis discusses closed-loop control of the hypnotic and the analgesic components of anesthesia. The objective of the work has been to develop a system which independently controls the intravenous infusion rates of the hypnotic drug propofol and analgesic drug remifentanil. The system is designed to track a reference hypnotic depth level, while maintaining adequate analgesia. This is complicated by inter-patient variability in drug sensitivity, disturbances caused foremost by surgical stimulation, and measurement noise. A commercially available monitor is used to measure the hypnotic depth of the patient, while a simple soft sensor estimates the analgesic depth. Both induction and maintenance of anesthesia are closed-loop controlled, using a PID controller for propofol and a P controller for remifentanil. In order to tune the controllers, patient models have been identified from clinical data, with body mass as only biometric parameter. Care has been taken to characterize identifiability and produce models which are safe for the intended application. A scheme for individualizing the controller tuning upon completion of the induction phase of anesthesia is proposed. Practical aspects such as integrator anti-windup and loss of the measurement signal are explicitly addressed. The validity of the performance measures, most commonly reported in closed-loop anesthesia studies, is debated and a new set of measures is proposed. It is shown, both in simulation and clinically, that PID control provides a viable approach. Both results from simulations and clinical trials are presented. These results suggest that closed-loop controlled anesthesia can be provided in a safe and efficient manner, relieving the regulatory and server controller role of the anesthesiologist. However, outlier patient dynamics, unmeasurable disturbances and scenarios which are not considered in the controller synthesis, urge the presence of an anesthesiologist. Closed-loop controlled anesthesia should therefore not be viewed as a replacement of human expertise, but rather as a tool, similar to the cruise controller of a car

Multi-stage stochastic optimization and reinforcement learning for forestry epidemic and covid-19 control planning

This dissertation focuses on developing new modeling and solution approaches based on multi-stage stochastic programming and reinforcement learning for tackling biological invasions in forests and human populations. Emerald Ash Borer (EAB) is the nemesis of ash trees. This research introduces a multi-stage stochastic mixed-integer programming model to assist forest agencies in managing emerald ash borer insects throughout the U.S. and maximize the public benets of preserving healthy ash trees. This work is then extended to present the first risk-averse multi-stage stochastic mixed-integer program in the invasive species management literature to account for extreme events. Significant computational achievements are obtained using a scenario dominance decomposition and cutting plane algorithm.The results of this work provide crucial insights and decision strategies for optimal resource allocation among surveillance, treatment, and removal of ash trees, leading to a better and healthier environment for future generations. This dissertation also addresses the computational difficulty of solving one of the most difficult classes of combinatorial optimization problems, the Multi-Dimensional Knapsack Problem (MKP). A novel 2-Dimensional (2D) deep reinforcement learning (DRL) framework is developed to represent and solve combinatorial optimization problems focusing on MKP. The DRL framework trains different agents for making sequential decisions and finding the optimal solution while still satisfying the resource constraints of the problem. To our knowledge, this is the first DRL model of its kind where a 2D environment is formulated, and an element of the DRL solution matrix represents an item of the MKP. Our DRL framework shows that it can solve medium-sized and large-sized instances at least 45 and 10 times faster in CPU solution time, respectively, with a maximum solution gap of 0.28% compared to the solution performance of CPLEX. Applying this methodology, yet another recent epidemic problem is tackled, that of COVID-19. This research investigates a reinforcement learning approach tailored with an agent-based simulation model to simulate the disease growth and optimize decision-making during an epidemic. This framework is validated using the COVID-19 data from the Center for Disease Control and Prevention (CDC). Research results provide important insights into government response to COVID-19 and vaccination strategies

Preclinical pharmacokinetic evaluation of novel antimalarial and antituberculosis drug leads

Preclinical pharmacokinetics relies on efficient and accurate screening to select clinical candidates from early leads. Poor pharmacokinetic interpretation can disadvantage drug discovery by promoting inadequate compounds and expelling potential drug candidates. Objectives of this project included pharmacokinetic evaluation of antimalarial and anti-tuberculosis lead compounds with techniques aimed at improving preclinical pharmacokinetic outcomes. This included mechanistic pharmacokinetic approaches such as non-linear mixed effects (NLME) modelling in comparison with traditional non-compartmental analysis. Where appropriate, pharmacokinetic methods were expanded to include organ distribution and capsule dosing in mice to bridge our techniques from discovery to early development. Three benzoxazole amodiaquine analogues possessing equipotent in vitro antiplasmodial activity and showed diverse in vivo efficacy in a malaria mouse model. Evaluation of their respective pharmacokinetics in mice showed their in vivo exposures could translate to in vivo efficacy. Retrospective PK/PD simulations point to a time above IC50 drive in efficacy. Pharmacokinetic evaluation of an aminopyridine antimalarial compound in its cyclodextrin inclusion complex revealed a pH dependent increase in solubility that reduced variance, likely due to favoured intestinal absorption. Investigation of two novel fusidic acid C-3 ester prodrugs aimed at repositioning fusidic acid for tuberculosis, showed high concentrations of the rodent specific 3-epifusidic acid metabolite that greatly reduced exposure of fusidic acid in mice. Further organ distribution studies showed a prodrug strategy is still viable for repositioning fusidic acid for tuberculosis, but that rodent models are inappropriate for further evaluation. NLME modelling successfully provided unique mechanistic and mathematical insight of pharmacokinetic profiles of new leads. The level of interpretation on pharmacology parameters improved and aided in understanding why drug leads are likely to fail or succeed, assisting future compound optimisation

Use of Kalman Filtering in State and Parameter Estimation of Diabetes Models

Diabetes continues to affect many lives every year, putting those affected by it at higher risk of serious health issues. Despite many efforts, there currently is no cure for diabetes. Nevertheless, researchers continue to study diabetes in hopes of understanding the disease and how it affects people, creating mathematical models to simulate the onset and progression of diabetes. Recent research by David J. Albers, Matthew E. Levine, Andrew Stuart, Lena Mamykina, Bruce Gluckman, and George Hripcsak1 has suggested that these models can be furthered through the use of Data Assimilation, a regression method that synchronizes a model with a particular set of data by estimating the system\u27s states and parameters. In my thesis, I explore how Data Assimilation, specifically different types of Kalman filters, can be applied to various models, including a diabetes model. 1Albers, David J, Matthew E Levine, Andrew Stuart, Lena Mamykina, Bruce Gluckman, and George Hripcsak. 2018. Mechanistic machine learning: how data assimilation leverages physiologic knowledge using bayesian inference to forecast the future, infer the present, and phenotype. JAMIA 25(10):1392–1401. doi:10.1093/jamia/ocy106. https: //doi.org/10.1371/journal.pone.0048058