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1360 Part H Automation in Medical and Healthcare Systems

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Part H 75

1361

Automation a

76. Automation and Control

in Biomedical Systems

Robert S. Parker

Biomedical systems are a complex collection of case

studies where the principles of automation and

control theory are seeing increased application.

This growing interest has a twofold motivation:

the need for advanced automation and treatment

design tools for use in medical practice and the

challenges inherent to biomedical systems and

clinical deployment of technology. This chapter

provides an overview of the automation, control,

and optimization tools used in the biomedical

arena. While the scope of potential applications

is vast, examples of biomedical treatment design

systems for cancer and insulin-dependent diabetes

are discussed. The chapter concludes by scratching

the surface of emerging areas in need of translated

or novel systems and automation tools.

76.1 Background and Introduction................1361

76.1.1 Scope ....................................... 1361

76.1.2 Data Quantity and Quality ........... 1362

76.1.3 Preclinical Versus Clinical Study

Data ......................................... 1363

76.2 Theory and Tools ..................................1364

76.2.1 Parameter Estimation ................. 1364

76.2.2 Pharmacokinetics....................... 1366

76.2.3 Modeling Physiology................... 1366

76.2.4 Biochemical Networks................. 1367

76.2.5 Model-Based Control

and Optimization ....................... 1368

76.3 Techniques and Applications .................1369

76.3.1 Type I Diabetes Modeling

and Control ............................... 1369

76.3.2 Cancer Radio- and Chemotherapy 1372

76.4 Emerging Areas and Challenges .............1373

76.4.1 in vitro Physiological Systems ...... 1374

76.4.2 Translating in vitro to in vivo....... 1374

76.4.3 Model and Network Structure

Identiﬁcation ............................ 1375

76.5 Summary .............................................1375

References ..................................................1375

76.1 Background and Introduction

76.1.1 Scope

Automation is ubiquitous in society today. Air con-

ditioners and thermostats keep the house cool; cruise

control maintains speed without driver intervention on

the road. Both of these examples demonstrate the ef-

ﬁciency of automating processes and taking advantage

of advanced control algorithms in doing so (the switch-

ing on and off of the air conditioner and the degree of

depression of the gas pedal are both handled by mathe-

matical algorithms).With advancements in engineering,

miniaturization, and computational capability, it be-

comes possible to deploy automation and control tools

in the medical arena.A common example of automation

in medicine is the pacemaker, with a quarter-million

per year implanted around the world [76.1]. Far from

the simple pacing instrument of 1932 [76.2], today’s

devices include diagnostic algorithms for real-time

analysis, adaptive response (control), and programma-

bility [76.1]. Robotics is a common tool used in medical

science, and due to its wide treatment elsewhere, this

topic is consciously excluded from the present chap-

ter (the interested reader is referred to [76.3] from the

Handbook of Industrial Robotics). This chapter will ex-

plore the use of mathematical models of the body (in

a variety of styles, see Sect.76.2 for more detail) as

well as tools from optimization and dynamic control

theory in the automation of medical treatment decisions

Part H 76

1362 Part H Automation in Medical and Healthcare Systems

and the deployment of treatment. While modeling the

body is certainly not a new concept – Teorell discussed

the use of mathematical modeling in drug distribution

as early as 1937 [76.4, 5] – the ability to deploy these

models is a relatively recent event given the continuing

advancement of computational power.

Medical practice, in a broad sense, is a disci-

pline grounded in ﬁnding feasible solutions to complex

biosystems problems. Typically, clinicians are pre-

sented with a set of symptoms from which they must

diagnose and treat a disease state. Treatments in-

duce response, and when drugs are administered, the

dose amount and schedule (timing of doses) elicit

a variety of measurable outcomes such as drug phar-

macokinetics (PK, drug concentration versus time) and

pharmacodynamics (PD, disease response and toxicity).

Schematically, this is the unshaded component shown

in Fig.76.1. The shaded region characterizes a model-

based control system that:

1. Compares the measurable responses to treatment to

a desired clinical response

2. Employs a control algorithm and mathematical

model of the patient to calculate an optimal treat-

ment

3. Deploys that treatment by adjusting dose and sched-

ule of administration

Typically, this last phase would be a recommen-

dation to a clinician who would be responsible for

the ultimate go/no-go decision of implementing the

Patient model

Control

algorithm

Desired

response

Treatment dose

and schedule Response to treatment

Fig. 76.1 Schematic of drug treatment. Unshaded: open-

loop, shaded: automated model-based decision support or

control system for treatment design and/or implementation

algorithm-recommended treatment. The challenge in

designing such automation systems is the complexity

of the underlying model of the patient and its incor-

poration within a rigorous mathematical framework for

optimizing or controlling the treatment outcome.

When translating systems tools to the clinical arena,

it is critical to work closely with clinicians. Unlike

a distillationcolumn, where automation tools can be im-

plemented as long as their performance is superior to

existing technology, the most likely early implementa-

tion of automation tools in the biomedical arena is as

decision support (a semi-closed-loop formulation). The

charge of the clinician to do no harm and the critical

issue of malpractice, with its corresponding ﬁnancial

and legal ramiﬁcations, are too signiﬁcant to allow

an automated system to operate independently without

oversight. Even a single failure resulting in the death of

a patient could halt the development and deployment of

an automated medical device. Hence, extensive clinical

trials demonstrating safety of patients (phase I trials),

efﬁcacy of the automated tool or decision support sys-

tem (phase II), and ultimately performance superiority

to current standards of practice (phase III/IV trials) will

be required before an automated device can be deployed

with conﬁdence in the biomedical arena.

The remainder of this section will establish chal-

lenges to automation in biomedical systems. The ﬁrst

set are data issues, including quantity available, quality,

resolution, and consistency and frequency of collection

(for use in dynamic modeling). The differences be-

tween preclinical andclinical data arediscussed, andthe

concept of patient tailoring (including interpatient vari-

ability and treatment individualization) is introduced.

The chapter continues in Sect.76.2 with the tools used

in biosystems automation and control. Two case study

areas are discussedin detailin Sect. 76.3, anda small se-

lection of directions of future research are summarized

in Sect.76.4.

76.1.2 Data Quantity and Quality

The amount of experimental biological data in the lit-

erature is vast. However, the size scale of the data is

equally large (spanning nanometers to meters and be-

yond). The dynamic character of the data varies as well,

including static baseline measurements, point measure-

ments of biological changes (the usual case in -omics

data), and dynamic PK proﬁles. A further complica-

tion captured in data sets such as those from DNA

microarrays is the complexity of theinteraction between

the various cellular processes, which is simultaneously

Part H 76.1

Automation and Control in Biomedical Systems 76.1 Background and Introduction 1363

Fig. 76.2 Expression response of MCF breast cancer

cells treated with vitamin D (ﬁgure originally published

in [76.6])

a complication in the use of modeling and automation

tools and a challenge to those interested in building new

tools. It is at this crossroads of biology and systems

engineering that computational models and automation

tools can make a signiﬁcant contribution by unifying the

collection of disparate data across multiple scales and

time points.

Facilitating the collection of this interacting dy-

namic data are advances in measurement technology

that provide higher-resolution information of the bio-

logical processes taking place. Microarray technology

has provided a more detailed picture of DNA and RNA

responses to external stimuli, and these tools may even-

tually provide a fully dynamic proﬁle of speciﬁc up-

and downregulation events in response to drug treat-

ment. The data available from a microarray study at

present, however, do not provide such a picture. The

measurements are often distributed in time, with sig-

niﬁcant nonmeasurement periods, so the dynamics of

a particular challenge may or may not be fully captured.

Furthermore, the ability to quantitate at high precision

with a microarray is limited by the sensing technology;

semiquantitative and directional information is avail-

able from the colorimetric technique, but exact counts

of DNA or RNA are unavailable (Fig. 76.2). Tissue

concentrations of agents are measured using high-

performance liquid chromatography (HPLC)asthegold

standard; however, liquid-chromatography mass spec-

troscopy (LC/MS) has higher resolution (the ability to

measure fewer ng/ml) and lower sample volume re-

quirements. Hence, PK data may be more accurate in

the future, thereby leading to higher-quality models and

possibly to better informed treatment decisions. A po-

tential drawback of this new technology is the need to

repeat previous studies with the new analysis device.

This may lead to conﬂicting outcomes between stud-

ies as superior measurements are analyzed, and model

reﬁnement should be an expected and ongoing process.

Given these factors, modelers and algorithm developers

must carefully evaluate the data sets they employ, and

the ongoing generation of new data, to be sure that the

treatment decisions recommended by automated tools

are of the highest possible quality and provide the great-

est potential beneﬁt to the patient.

76.1.3 Preclinical Versus Clinical Study Data

Animal studies are a required precursor to human tri-

als for testing novel drugs. These studies are primarily

toxicologic – the objective is to establish toxicities as-

sociated with drug administration and to evaluate the

various routes of administration (oral, intravenous (IV),

etc.). Pharmacokinetic studies are often required by the

USFood and Drug Administration (FDA) [76.7]. The

search for tissue toxicities leads to detailed (potentially

dynamic) concentration versus time data sets from an-

imals for compounds of potential clinical interest. The

outcome of these studies is often a maximum tolerated

dose (MTD) in a variety of species, and the dose for

ﬁrst trials in humans is established from the MTD in

the most sensitive species. A useful side-effect of these

studies, from an automation perspective, is the value of

these data sets in modeling dynamic biological phenom-

ena at scales that one cannot achieve in humans (e.g.,

tissue drug concentration versus time via destructive

analyses such as HPLC or LC/MS).

Animals are not people, however. A critical short-

coming of model-based automation and control for the

biosystems ﬁeld is that most of the high-resolution

(temporal or spatial) in vivo data comes from animal

studies. Hence, detailed animal models of pharma-

cokinetic and pharmacodynamic behavior can be con-

Part H 76.1

1364 Part H Automation in Medical and Healthcare Systems

structed [76.8–10], but the scale-up of these models to

humans remains open (Sect. 76.4.2). In contrast, phar-

macokinetic and pharmacodynamic models collected in

clinical trials are often macroscopic in nature. Measure-

ments are often easy to collect and analyze, such as

urine measurements of function or disease state (e.g.,

creatinine clearance or glucose in the urine), drug con-

centrations in the bloodstream, or circulating markers

of effect (e.g., cholesterol levels in the blood). Further-

more, measurements from a single patient are often not

a full time course but a small number of samples (1–3)

that provide the clinically necessary information but

may not provide sufﬁcient data to inform a model of the

individualpatient. Depending on the resolution and pre-

diction required from the model, as well as its intended

use, a variety of tools may be employed in model syn-

thesis and deployment at the animal and human scales.

These are outlined in the next section.

76.2 Theory and Tools

This section will introduce some of the tools often

employed in the solution of biomedical modeling and

control problems. A summary can be found in Ta-

ble 76.1, which provides the section where a technique

can be found, the name of the technique(s) discussed

therein, and one or two strengths and weaknesses of the

various approaches. While this is certainly not a canon-

ical summary of tools employed in the automation of

biomedical problems, this section provides a jumping-

off point for further reading on the tools and techniques

of biomedical control.

76.2.1 Parameter Estimation

In the development of mathematical models, there

is a need to estimate parameters. A concern in this

arena is the a priori identiﬁability of such models.

Phrased differently: are the parameters of a given model

structurally identiﬁable from the available input–output

(measurement) data? A number of recent studies of

biomedically relevant systems explore tools and meth-

ods for evaluating model identiﬁability [76.11–15].

Audolyet al. [76.11]examine the identiﬁabilityof linear

models, which has direct mapping to the popular com-

partmental models used in pharmacokinetic analysis

(Sect.76.2.2). Physiological structures are the focus of

Yates [76.13], who examines the global identiﬁabilityof

the model with restriction to local identiﬁability in the

worst case. In a nonlinear context, Bellu et al. [76.15]

provide differential algebra for identiﬁability of sys-

tems (DAISY) software to evaluate the identiﬁability

of nonlinear systems in polynomial or rational form

(a limitation of the differential algebra tool used in the

analysis). At a higher level of resolution, intracellular

networks are of particular interest in systems biology,

metabolomics, etc. The amount of information that is

measurable from these systems is generally small with

respect to the parameters. Va n R iel and Sontag [76.14]

explore methods of modeling these systems using ﬁcti-

tious dependentinputs to represent unidentiﬁable model

structures.

Given an identiﬁable model, parameter values need

to be estimated with conﬁdence. Two common objec-

tives for parameter estimation include nonlinear least

squares and maximum likelihood. The nonlinear least-

squares technique can be represented as [76.16]

min

J(p) =



i=1

(i)−

y(i, p)]

. (76.1)

The measured data at observation i is given by y

(i),

the model prediction at the same observation, which

depends on parameters p, is represented by

y(i, p),

and the observation-dependent weights are given by

. In the unweighted case χ

reduces to an appropri-

ately sized identity matrix. Alternatively, weighting by

= 1/σ

can reduce sensitivity to variability in the

measurement, where σ

is the standard deviation of the

measurement data at observation i. Standard tools for

this optimization using linear or nonlinear models are

available in MATLAB [76.17]. An alternative objective

is maximum-likelihood estimation (MLE), a statistical

approach to parameter estimation. Here, the conditional

probability that the residuals are generated by a model

having the estimated parameter vector is maximized.

The likelihood function is written as [76.16]

L(p) =

i=1

√

2π



−1



(i)−

y(i, p)





(76.2)

The objective is to maximize the likelihood that the

data results from the model (as parameterized by the

vector p). Assumptions in using maximum likelihood

include: (i) uncorrelated residuals and (ii) residuals are

Part H 76.2