In Silico Models of Alcohol Dependence Treatment: A Stochastic Approach

Models to Measure

The models to measure are generally simpler, allowing hidden relationships to be evaluated by estimating underlying parameters. Most of these models are compartmental; for example, they represent the human body as a set of homogeneous compartments of specific concentrations and volumes linked by diffusion or rate-limited pathways. Classic examples include the Widmark Model of Ethanol Pharmacokinetics, which offers a straightforward interpretation with a constant ethanol clearance rate and the human body modeled as one compartment, and the more complex Minimal Model of Glucose Kinetics suggested by Bergman and Cobelli more than 30 years ago to measure insulin resistance in health and diabetes. More recently, with the advent of the digital biology paradigm, various models to measure have been developed that address pharmacokinetics, physiology, and human behavior. Deeper understanding of the processes involved and the development of novel measuring tools have allowed for more precise measurement of ethanol pharmacokinetics and the development of more complex nonlinear models. The study of ethanol kinetics in vivo has led to a better representation of the ethanol-aldehyde-acetate process via the Michaelis-Menten rate of alcohol clearance introduced by Norberg. It is now widely accepted that alcohol clearance is a Michaelis-Menten–controlled reaction—that is, an enzyme-enhanced chemical reaction with limited supply. These and other pharmacokinetic models are discussed in detail in Chapter 63 .

Models to Simulate

The models to simulate are maximal multiparameter models that describe the complexity of the system as comprehensively as possible. For example, the meal model of glucose-insulin dynamics is a descendant of the Minimal Model, which encompasses several metabolic subsystems including the gastrointestinal tract, renal function, hepatic glucose production, and others. When a maximal model is built, the computer simulation of the observed biosystem becomes possible, leading to in silico trials involving virtual “subjects” rather than real people. Such in silico trials can serve as precursors, guiding expensive and time-consuming clinical investigations by outruling ineffective treatment approaches. For example, our simulator of the human metabolic system has received US Food and Drug Administration (FDA) approval and recognition for the preclinical testing of control strategies in artificial pancreas studies. Using this simulator, the time for refining and safety testing of new algorithms that target the closed-loop control of diabetes has been reduced from years to several months. Therefore, realistic computer simulation is capable of providing valuable information about the effectiveness, safety, and limits of various treatments. Computer simulation allows experiments with extreme situations and testing of extreme failure modes that are unrealistic in animals and clinically impossible in humans. In addition to extreme experiments, various treatment scenarios can be efficiently tested and either rejected or accepted for inclusion in future clinical experiments, which allows for rapid, comprehensive, and cost-effective clinical trial design. We need to emphasize, however, that good in silico performance of a treatment does not guarantee in vivo performance. Computer simulation should be used only to reject inefficient treatments; it cannot confirm the efficacy of an intervention.

Models to Control

External control of a complex technical or living system is generally achieved by control algorithms that are based on a certain mathematical representation of the system—a model to control—combined with the ability to observe the system in real time and make immediate decisions for correction of the system state. The models to control are typically simplified (frequently linearized) models that allow for rapid observation and computation of the corrective action. A prime example of medical devices that use adaptive control algorithms is the cardiac pacemaker, which in the past two decades has been incorporating automated control functions such as automatic capture and sensing control, self-adjusting rate response settings, sinus rhythm and atrioventricular conduction preference, and others. In diabetes, successful attempts at external closed-loop control have been made using various systems and algorithms, from cumbersome intravenous systems and implantable devices to external subcutaneous control, and portable “artificial pancreas.” Relating control to simulation, comprehensive in silico testing of control algorithms is an efficient strategy if a model to control is tested against a much more complex model to simulate. In other words, the effectiveness of a controller can be judged if it is tested in realistic in silico conditions, which can be achieved by a comprehensive simulation model.

Formal Description of Human Behavior and Social Conditioning

In the context of in silico models of alcohol dependence treatment, applicable quantitative strategies include models to measure and models to simulate. In order to build such models, a formal mathematical description of human behavior and environmental conditioning is needed. However, the behavioral and social modeling field is still quite limited. Although theoretical models based on internal somatic perception have been proposed, their heuristic approach has not permitted their development in sufficient mathematical detail to guide data analysis. For example, the stages of change described by the Transtheoretical Model of DiClemente and Prochaska refer to a stochastic sequence—of readiness to change, stage of change status, temptation, and confidence—that has consistently shown predictive and explanatory ability for clinical outcome in alcohol dependence treatment studies. However, this sequence has not been identified as stochastic and has not been formalized to the extent that would permit computerized assessment and simulation. Another example can be provided in the context of nonspecific treatment effects, such as the Hawthorne effect, which describes the tendency of an individual to change his or her behavior as a consequence of being observed or studied. Although this effect provides evidence for the importance of environmental conditioning and external reinforcement for all stages of the progression of alcohol dependence—from acquisition of alcohol dependence, through treatment, to potential relapse—there is no formal description of environmental conditioning that would allow its inclusion in an integrated in silico model encompassing physiology, behavior, and social conditioning. Therefore, to advance the field, we have proposed a formal stochastic bio-behavioral model of the sequence leading to self-regulation decision, in which the first three steps of the process were described by continuous variables, whereas the decisions at Step 4 were binary. The general concept is that decisions concerning self-regulation behaviors are often based on perception and appraisal of the body’s internal state. Thus the sequence preceding a certain action includes at least four sequential steps: internal condition ➔ perception ➔ environmental conditioning ➔ self-regulation decision. We have applied this general framework to evaluate the relationship between self-treatment behavior and the development of hypoglycemia in diabetics, as well as the psycho-physiological factors associated with the attention impairment experienced by those with attention-deficit/hyperactivity disorder.

Combining Biology and Behavior In Silico

In this chapter, we view alcohol dependence and the response to alcohol dependence treatment as a recurrent bio-behavioral process developing in time. Such an approach captures the dynamics of sequential changes occurring during acquisition of alcohol dependence, successful treatment, or relapse. We provide a rigid mathematical framework formally describing these dynamics. To do so, we first introduce a stochastic model of behavioral and social conditioning, describing the frequently random ^a

a Here we need to make a distinction between the lay and scientific understanding of randomness: scientifically, a random variable is a variable that can assume a set of values with certain probabilities comprising its distribution. For example, any constant is a random variable assuming its only value with probability 1 and all other values with probability 0. Other random variables have normal (Gaussian) distribution; others have uniform distribution, and so on. The lay understanding of randomness typically refers to uniformly distributed variables that can assume any of multiple values with equal probabilities..

effects of human behavior and social reinforcement. We then merge this stochastic model with the deterministic model of alcohol metabolism described in Chapter 63 . In combination, these two models provide the background for in silico interpretation of biology and behavior in their relationship to treatment effect. To formally represent behavioral and social conditioning, we identify several sequential steps. Each step is represented by a probability distribution, and the set of these distributions across all steps regulates the feed-forward relationships of the process from internal condition to self-regulation decision. Each person is represented by an individual treatment-effect profile, defined as the set of transition probabilities between the sequential steps of the model specific to that person. This model serves as a stochastic behavioral generator of events, each event being a drink, which is supplied as an input to an individualized model of alcohol metabolism. In other words, the in silico experiments with alcohol dependence treatment use behavioral and social parameters that serve as generators of metabolic disturbances to the system (person), which are then processed through an individualized metabolic model, thereby allowing the formal decomposition and reconstruction of the patterns of drinking behavior and their modulation by placebo or medication treatment. We illustrate our proposed approach by re-analyzing data from a study of ondansetron for the treatment of alcohol dependence and include in the model the nonspecific placebo effects that occurred before the active treatment phase of the study, with a special emphasis on the highly significant differences between heavy and nonheavy drinkers observed during the study.