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As a natural development of previous cancer chemotherapy case study material, the current paper presents two further case studies. The first case study describes a technique for obtaining a framework of specific model parameter values, corresponding to realistic tumour growth rates, for the Generalized-Ex (GE) tumour growth model. Subsequently the second case study demonstrates how oncologists can employ this framework, in conjunction with arecently developed competition model, for investigating tumour response to chemotherapy.

In recent years much work has been don; on modelling the growth of malignant tumours with a view to predicting their development and optimizing the effect of chemotherapy on their alleviation. One approach has been to treat the tumour as a non‐linear system and the drug treatment as a control strategy. This has led to the use of calculus of variations and optimization techniques to produce optimal and sub‐optimal drug regimes. This paper uses the Gompertz equation as a model of tumour growth and attempts to minimize final tumour size in the presence of drug constraints. The optimization technique is novel in that it uses the genetic algorithm method to devise optimal drug regimes where individual regimes are encoded as ‘chromosomes’ and evolution is simulated in order to improve treatment strategies.

In this paper we study the effects of drug resistance on the optimal schedule of two drugs in cancer chemotherapy. We analytically determine the optimal schedule when the tumour grows exponentially and the drugs have a linear killing action. Drug resistance enters into the model via an effectiveness term that decreases the cell loss as the accumulation of the drug grows.

Cyclophosphamide along with recombinant human granulocyte-colony stimulating factor (rhG-CSF) is a commonly used strategy for mobilization. However, the optimal timing for rhG-CSF initiation after cyclophosphamide has not been determined as conclusively as has the G-CSF dose. In this paper, we aimed to present gradual dose increment of rhG-CSF between the third day of mobilization and time to apheresis that is started with 5μg/kg (from day 3 to day 7) and continued with 10μg/kg (from day 8 to time to apheresis) for peripheral blood stem cell (PBSC) mobilization in multiple myeloma (MM) patients and its effect on stem cell yield and mobilization success. Data from 30 consecutive patients with MM who underwent PBSC mobilization between October 2011 and June 2013, were retrospectively reviewed. While twenty-eight of 30 patients (93.3%) were successfully mobilized, 2 patients (6.7%) had mobilization failure. The final median CD34+ cell dose harvested from the patients was 9.5×106/kg. The median number of apheresis was 2.5 (range, 0-3). Twenty-four patients (80%) yielded >2×106 CD34+ cells/kg in one apheresis procedure. In conclusion, our regimen might be used to decrease the mobilization failure regarding the low dose rhG-CSF use and provide a cost effective strategy. © 2013 Elsevier Ltd.

Cancer chemotherapy is a complicated treatment that needs balancing the cell killing with the undesirable toxic sideeffects caused by the drugs. Several computational methods have been used in finding the right balance. This paper presents a model-based method to design optimum chemotherapy treatment schedule by using multiple objective particle swarm optimization (MOPSO) algorithm. Three dose plans are designed based on a widely used cancer chemotherapy model. MOPSO algorithm is employed for determining drug schedule with suitable parameters that give the dose levels and their occurrences. Besides tumor cell population, the model also shows other important parameters, such as natural killer cells (NK), tumorspecific-cytotoxic-T (CD8+ T) cell population and circulating lymphocyte population. Results show that the designed method can generate efficient drug scheduling that can eliminate the tumor cells with acceptable toxicity. The designed drug schedules also satisfy important treatment constraints and other objectives.

In a chemotherapy scheduling process a chemotherapy is a treatment of cancer using a set of toxic drugs. In the paper we propose a Decision Support System for the anti-cancer medical treatment to improve physicians’ decisions about drugs doses selection and scheduling. A hybrid meta-heuristic algorithm has been applied to the problem of bi-criteria optimization allowing to find effective chemotherapy drugs dose scheduling as the minimization of a tumor size at a fixed period of time and maximization of Patient Survival Time. The numerical tests of proposed algorithm gives the possibility of producing a set of alternative treatment scenarios according to the final decision.

This paper presents a design method for optimal cancer chemotherapy schedules using genetic algorithm (GA). The main objective of chemotherapy is to reduce the number of cancer cells or eradicate completely, if possible, after a predefined time with minimum toxic side effects which is difficult to achieve using conventional clinical methods due to narrow therapeutic indices of chemotherapy drugs. Three drug scheduling schemes are proposed where GA is used to optimize the doses and schedules by satisfying several treatment constraints. Finally, a clinically relevant dose scheme with periodic nature is proposed. Here Martin’s model is used to test the designed treatment schedules and observe cell population, drug concentration and toxicity during the treatment. The number of cancer cells is found zero at the end of the treatment for all three cases with acceptable toxicity. So the proposed design method clearly shows effectiveness in planning chemotherapy schedules.

We explore mathematical properties of models of cancer chemotherapy including cell-cycle dependence. Using the mathematical methods of control theory, we demonstrate two assertions of interest for the biomedical community: 1 Periodic chemotherapy protocols are close to the optimum for a wide class of models and have additional favourable properties. 2 Two possible approaches, (a) to minimize the final count of malignant cells and the cumulative effect of the drug on normal cells, or (b) to maximize the final count of normal cells and the cumulative effect of the drug on malignant cells, lead to similar principles of optimization. From the mathematical viewpoint, the paper provides a catalogue of simplest mathematical models of cell-cycle dependent chemotherapy. They can be classified based on the number of compartments and types of drug action modelled. In all these models the optimal controls are complicated by the singular and periodic trajectories and multiple solutions. However, efficient numerical methods have been developed. In simpler cases, it is also possible to provide an exhaustive classification of solutions. We also discuss developments in estimation of cell cycle parameters and cell-cycle dependent drug action.

An optimal control problem for combinations of cancer chemotherapy with immunotherapy in form of a boost to the immune system is considered as a multi-input optimal control problem. In the objective, a weighted average of several quantities that describe the effectiveness of treatment is minimized that includes the number of cancer cells at the terminal time, a measure for the immunocompetent cell densities at the terminal point as a negative term, the overall amount of therapeutic agents given as a measure for the side effects of treatment and a small penalty on the terminal time which is free. In the dynamics, a pharmacokinetic model for the concentrations of the pharmaceutical agents is included. The focus of the paper is on the structure of singular arcs. Their local optimality properties will be investigated analytically and then illustrated through numerically obtained results. The resulting geometric structure of optimal controlled trajectories provides some insights into the design of optimal protocols, especially the sequencing of the drugs in these combination treatments.

The paper presents some properties and. related simulation results of optimal control strategies in problems arising from cancer chemotherapy. Two bilinear models of a proliferation cycle are considered. A numerical gradient method is applied to solve the problems.

Introduction: Nasopharyngeal carcinoma can present with different neurological signs and findings. In this paper, we report a patient presenting with Horner syndrome and syncopal episodes who was finally diagnosed with nasopharyngeal carcinoma. Case Report: A 56-year-old man presented with a history of slowly progressive right upper-eyelid droop for the last 1.5 months and episodes of loss of consciousness. After detailed clinical and laboratory examinations, the patient had the final diagnosis of metastatic nasopharyngeal carcinoma. Conclusions: This is the first case with nasopharyngeal carcinoma presenting with both Horner syndrome and carotid-sinus syncope. The mechanism of Horner syndrome and the syncopal episodes and their relation with the lesion location are discussed.Copyright © 2012 by Lippincott Williams & Wilkins.

This paper introduces a new hybrid Genetic Algorithm (GA) crossover approach, Targeted EDA (TEDA), that combines a targeted intervention principle with Estimation of Distribution Algorithms (EDA) to solve optimal control problems. The approach is suited to tasks where the number of interventions used is an important part of solution fitness and includes problems such as cancer chemotherapy scheduling. Fitness Directed Crossover (FDC) is a modified GA crossover method that actively drives the number of selected control interventions towards those of a fitter individual. EDA are able to find fit solutions by discovering patterns within a population of selected individuals. TEDA uses FDC to select a suitable number of interventions to use while using an EDA based approach to select which interventions to set. Results suggest that by combining the two approaches, TEDA is able to outperform both EDA and FDC on a sample optimal control problem.

In this paper we study the application of a single drug in cancer chemotherapy where the drug can block progression through the cell cycle. Under certain conditions we determine the regimen that reduces the tumor population to a desired endpoint in minimal time while keeping a normal cell population above some lower limit. We also study the sensitivity of the optimal solution to various parameters.

In the paper, it has been proved that some optimal control problems resulting from the simplest models of cancer chemotherapy lead to singular control solutions. The singularity seems to be a characteristic feature of bilinear models even if dynamics of the drug activity or reaction of critical normal tissues is encountered. On the other hand, the singular control is absent when the Gompertz-type growth is applied.

Recently, reinforcement learning methods have drawn significant interests in the area of artificial intelligence, and have been successfully applied to various decision-making problems. In this paper, we study the applicability of the NAC (natural actor-critic) approach, a state-of-the-art reinforcement learning method, to the drug scheduling of cancer chemotherapy for an ODE (ordinary differential equation)-based tumor growth model. ODE-based cancer dynamics modeling is an active research area, and many different mathematical models have been proposed. Among these, we use the model proposed by de Pillis and Radunskaya (2003), which considers the growth of tumor cells and their interaction with normal cells and immune cells. The NAC approach is applied to this ODE model with the goal of minimizing the tumor cell population and the drug amount while maintaining the adequate population levels of normal cells and immune cells. In the framework of the NAC approach, the drug dose is regarded as the control input, and the reward signal is defined as a function of the control input and the cell populations of tumor cells, normal cells, and immune cells. According to the control policy found by the NAC approach, effective drug scheduling in cancer chemotherapy for the considered scenarios has turned out to be close to the strategy of continuing drug injection from the beginning until an appropriate time. Also, simulation results showed that the NAC approach can yield better performance than conventional pulsed chemotherapy.

The paper presents a novel approach to optimising cancer chemotherapy with respect to conflicting treatment objectives aimed at reducing the number of cancerous cells and at limiting the amounts of anti-cancer drugs used. The approach is based on the Particle Swarm Optimisaion (PSO) algorithm that decomposes a multi-objective optimisation problem into several scalar aggregation problems, thereby reducing its complexity and enabling an effective application of Computational Intelligence techniques. The novelty of the algorithm is in providing particles in the swarm with information from a set of defined neighbours and leaders that assists in finding versatile chemotherapeutic treatments.

This paper presents a novel method of multi-drug scheduling using multi-objective genetic algorithm (MOGA) that can find suitable/optimum dosages by trading-off between cell killing and toxic side-effects of chemotherapy treatment. A close-loop control method, namely Integral-Proportional-Derivative (I-PD) is designed to control dosages of drugs to be infused to the patient’s body and MOGA is used to find suitable parameters of the controller. A cell compartments model is developed and used to describe the effects of the drugs on different type of cells, plasma drug concentration and toxic side-effects. Results show that specific drug schedule obtained through the proposed method can reduce the tumour size nearly 100% with relatively lower toxic side-effects.

This paper analyzes a mathematical model for the growth of bone marrow cells under cell-cycle-speci c cancer chemotherapy originally proposed by Fister and Panetta [8]. The model is formulated as an optimal control problem with control representing the drug dosage (respectively its eff ect) and objective of Bolza type depending on the control linearly, a so-called $L^1$-objective. We apply the Maximum Principle, followed by high-order necessary conditions for optimality of singular arcs and give sufficient conditions for optimality based on the method of characteristics. Singular controls are eliminated as candidates for optimality, and easily veri able conditions for strong local optimality of bang-bang controls are formulated in the form of transversality conditions at switching surfaces. Numerical simulations are given.

The main aim of cancer chemotherapy is to minimize the number of cancer cells after a number of fixed treatment cycles with minimum toxic side effects. Chemotherapy Scheduling increases the effectiveness of greater cell kill, decreases the chance of drug resistance and reduces any toxic side effects. Mathematical models for cancer chemotherapy are designed to predict the number of tumour cells and control the tumour growth during treatment. This requires an understanding of the system in absence of treatment and a description of the effects of the treatment. This paper presents an investigation into the development of a model for optimal chemotherapy scheduling to control tumour growth. We applied the integral, proportional and derivative (I-PD) controller based on Martin model of drug concentration to control the tumour growth and the drug toxicity by scheduling the drug dosages. The results of the different drug scheduling patterns in the present model offer better performance as compared to the existing models in implementing optimal chemotherapy treatment.

In this paper we consider the problems of modeling the tumor growth and optimize the chemotherapy treatment. A biologically based model is used with the goal of solving an optimization problem involving discrete delivery of antineoplastic drugs. Our model is formulated via compartmental analysis in order to take into account the cell cycle. The cost functional measures not only the final size of the tumor but also the total amount of drug delivered. We propose an algorithm based on the discrete maximum principle to solve the optimal drug schedule problem. Our numerical results show nice interpretations from the medical point of view.

This paper discusses the effects of using directed intervention crossover approaches with Genetic Algorithms (GA) and demonstrates their application to scheduling of bio-control agents and cancer chemotherapy treatments. Unlike traditional approaches such as Single Point Crossover (SPC) or Uniform Crossover (UC), the directed intervention techniques actively choose the intervention level based on the fitness of the parents selected for crossover. This work shows that a fitness directed intervention crossover approach leads to significant improvements over SPC and UC when applied to the two different scheduling problems.

This paper discusses the effects of mutation and directed intervention crossover approaches when applied to the derivation of cancer chemotherapy treatment schedules. Unlike traditional Uniform Crossover (UC), the directed intervention techniques actively choose the intervention level based on the fitness of the parents selected for crossover. This work describes how directed intervention crossover principles are more robust to mutation and lead to significant improvement over UC when applied to cancer chemotherapy treatment scheduling.

Ribonucleotide Reductase (RNR) plays a critical role in DNA synthesis, and is a well-recognized target for cancer chemotherapeutic and antiviral agents. RNR inhibition precludes DNA transcription and repair, from which results cell apoptosis. Many regulation checkpoints concerning RNR activity have been unravelled through the last two decades, with potential use to inhibit enzyme activity. This was accomplished by researchers from different but complementary areas, and from which several and different inhibitors have resulted. The volume of these studies has generated over 4000 articles since the discovery of RNR in 1960. This review summarises patents and papers during the period 1958 - 2005 dealing with the present understanding of ribonucleotide reductase biochemistry, mechanism of action and the most relevant data concerning RNR inhibition. Special attention is given to the inhibitors that have been patented and are currently in clinical use.

In this paper we consider the problems of modellinq the tumour growth and optimizing a combination chemotherapy. Our approach consists in constructing a Multicompartmental model based on cell kinetics and estimating its parameters by using data collected from in vitro measurements of tumour population. An optimal control problem with a cost functional reflecting the patient's lite expectancy, i.e., a compromise between the final tumour size and toxicity effects, is formulated and an iterative algorithm searching a multidrug dosage schedule satisfying necessary conditions of optimality is constructed. Although no systemic considerations are taken into account (and therefore a comparison with clinical experience is difficult) this approach is considered as a valuable step towards the building of a decision support system.

This book is a composite of a group of papers presented at a symposium of the Royal Society of Medicine on Sept 18, 1961. It opens with a review of the biochemistry and pharmacology of methotrexate and the clinical significance of some recent work. This is followed by a section on the systemic use of the agent, both alone and in combination with other cancer chemotherapeutic agents; there are included sections on the infusion use of methotrexate and the intrathecal use in acute leukemia involving the central nervous system. The material presented is a very worthwhile addition to the library of those interested in cancer chemotherapy. In particular, the discussion section following each major group of papers is very worthwhile, as it presents the review of many experts in these fields. The papers are fully reproduced with colored or black-and-white photographs, as well as charts. This symposium adequately points out

This volume is a compilation of the Proceedings of the Sixth National Cancer Conference held in Denver in September 1968. Sponsored by the American Cancer Society, Inc and the National Cancer Institute, the meeting was devoted to discussions of basic problems and advances in cancer surgery, lymphomas, leukemias, breast cancer, genitourinary cancer, radiation therapy, female pelvic cancers, cancers, gastrointestinal cancers, skin cancers, cancer chemotherapy and hormone therapy, head and neck cancers, skeletal tumors, soft part tumors, and lung cancer. Each of these topics is dealt with in an appropriate section of the book which contains the papers presented at these various sessions. The spectrum of etiology, diagnosis, and management of the specific tumor-types are discussed and advances in chemotherapeutic, surgical, and radiotherapeutic management are presented. The papers are easy to read, concise, clearly presented, and each is well illustrated with figures, photomicrographs, charts, and tables. This volume can be

Gemcitabine is a deoxycytidine analogue that has a broad spectrum of antitumour activity in many solid tumours including pancreatic cancer. We have recently carried out a pharmacogenomic study in cancer patients treated with gemcitabine, and found that one genetic polymorphism of an enzyme involved in gemcitabine metabolism can cause interindividual variations in the pharmacokinetics and toxicity of this agent. In this paper, we review recent genetic studies of gemcitabine, and discuss the possibility of individualised cancer chemotherapy based on a pharmacogenomic approach.

In this paper, we propose a novel fast evolutionary algorithm -- cycle-wise genetic algorithm (CWGA) based on the theoretical analyses of a drug scheduling mathematical model for cancer chemotherapy. CWGA is more efficient than other existing algorithms to solve the drug scheduling optimization problem. Moreover, its simulation results match well with the clinical treatment experience, and can provide much more drug scheduling policies for a doctor to choose depending on the particular conditions of the patients. CWGA also can be widely used to solve other kinds of the real dynamic systems.

Cancer chemotherapy is the treatment of cancer using drugs that kill the cancer cells, when the drugs are administered either orally or through veins. The drugs are delivered according to a schedule so that a particular dosage of drug level is maintained in the body. The disadvantage of these drugs is that they not only kill the cancer cells but also kill the normal healthy cells. The role of optimal control in chemotherapy is to maintain an optimum amount of drug level in the body so that only cancer cells are killed and hence the effect of drug on the healthy cells is minimized. Three different mathematical models for cancer growth are considered: log-kill hypothesis, Norton-simon model, and Emax model. Two different cost functions are considered for constrained and unconstrained optimal control, respectively. An open loop optimal control strategy has been reported in the literature. In this paper, a closed-loop optimal control strategy is addressed using all the three models and for both the cases of constrained and unconstrained drug delivery. For the unconstrained case the original nonlinear model has been linearized and the closed loop design is obtained by using matrix Riccati solutions. On the other hand, for the constrained case the original nonlinear model has been used to obtain closed loop optimal control using bang-bang strategy. Final simulation results show the advantages of closed loop implementation in terms of simpler and elegant controller design and incorporating the effect of current state variations.Copyright © 2007 by ASME

In this paper, we propose two new anticancer drug scheduling models with different toxicity clearances according to kinetics of enzyme-catalyzed chemical reactions. We also present a sophisticated automating drug scheduling approach based on evolutionary computation and computer modeling. To explore multiple efficient drug scheduling policies, we use a multimodal optimization algorithm - adaptive elitist-population based genetic algorithm (AEGA) to solve the models, and discuss the situation of multiple optimal solutions under different parameter settings. The simulation results obtained by the new models match well with the clinical treatment experience, and can provide much more drug scheduling policies for a doctor to choose depending on the particular conditions of the patients.

Microtubules (MT) are dynamic polymers that rapidly transition between states of growth, shortening, and pause. These dynamic events are critical for many microtubule functions such as intracellular trafficking and signaling. In addition, cancer chemotherapy drugs that target microtubules, such as the taxanes and the vinca alkaloids, are known to suppress microtubule dynamics at low doses, leading to mitotic arrest and cell death. Quantification of microtubule dynamics can be used as a read-out of anticancer-drug activity and can be a surrogate marker of drug sensitivity/resistance. The emerging nanotechnology such as quantum dots has provided properties such as less photo bleaching, higher probe imaging intensity, better specificity and sensitivity, which finally makes visualizing subcellular events over long enough time a possibility. But it also results in big increase in data acquisition. The traditional way of annotating MT manually is becoming a daunting task. Thus, the goal is to research and develop an efficient, reliable, and rapid MT tracking. In this paper, we describe active contour-based tracking methods to automatically track MT. We redefine the internal energy terms specifically for open snake, and examine different external energy terms for locating the end tips of a microtubule. This algorithm has been validated using simulated images, images of untreated MCF-7 breast cancer cells, and image of cells treated with the microtubule-targeting chemotherapeutic agent, Taxol.

Pronucleotides represent a promising alternative to improve the biological activity of nucleoside analogs in antiviral and cancer chemotherapy. In addition, pronucleotides are valuable tools for studies regarding the nucleoside/nucleotide metabolism. The aim is to achieve nucleotide delivery into cells and thereby bypass limitations during intracellular formation of nucleotides from their nucleoside precursors. The cycloSal approach is one of several conceptually different pronucleotide systems known but is the only approach in which a pronucleotide is cleaved successfully by simple but selective chemical hydrolysis. The basic concept, chemistry, different structural modifications, and their effects on the antiviral potency of the cycloSal d4TMP triesters are briefly discussed first. Then, the application of the approach to various biologically active nucleoside analogs against different targets is summarized. In the second part, the results of a conceptual extension of the cycloSal approach are presented: once cycloSal pronucleotides have passed the membrane, they should be trapped inside the cells after an enzyme-catalyzed process and then release the nucleotide. Finally, results are summarized that demonstrate that the cycloSal approach is not restricted to the delivery of bioactive nucleotides but is also applicable to the intracellular delivery of hexose-1-phosphates. Chemical synthesis, biophysical studies, and biological evaluation will be discussed in combination throughout this paper to demonstrate the strength of the cycloSal approach. (© Wiley-VCH Verlag GmbH & Co. KGaA, 69451 Weinheim, Germany, 2006)

In this paper, we present an apprmh to model the tumour growth and optimize a combination chemotherapy. We propose the use of in vitro data to estimate the parameter values of a multicompartmental model based on cell kinetics and incorporating the effect of drugs under consideration. The optimal control problem consists in finding the combination chemtherapy which minimizes a custom defined cost function, reflecting a compromise between toxicity effects and tumour growth, while satisfying dynamic and static constraints. A search direction algorithm seeking a multidrug dosage schedule satisfying the necessary conditions of optimality is constucted.

A major need in cancer chemotherapy is the availability of cancer cell-specific drugs. This paper discusses recent advances and perspectives in the field of selective drug recognition considering the key targets tyrosine kinases, DNA-topoisomerases and telomerase.

This paper presents the optimal control chemotherapy scheduling in cancer using neural networks. Unlike conventional methods, the proposed neural networks methodology, feedback linearization, is simple and capable of automatically finding the optimal solutions for complex cancer chemotherapy problems. Also, it allows the application of the well developed standard linear control techniques. Simulation results produce excellent control of drug dosage regimens in cancer chemotherapy, which are better than the solutions published in literature. Using specific chemotherapy concepts like dose size, dose intensity, and cumulative dose, allows the design of realistic dosage regimens from the optimal control computed by neural networks.

Cancer chemotherapy is a complex treatment mode that requires balancing the benefits of treating tumours using anti-cancer drugs with the adverse toxic side-effects caused by these drugs. Some methods of computational optimisation, Genetic Algorithms in particular, have proven to be useful in helping to strike the right balance. The purpose of this paper is to study how an alternative optimisation method – Particle Swarm Optimisation – can be used to facilitate finding optimal chemotherapeutic treatments, and to compare its performance with that of Genetic Algorithms.

A mathematical model for cancer chemotherapy treatment with a single G/sub 2//M specific killing agent is considered as an optimal control problem. The control represents the drug dosage of a single chemotherapeutic agent and the drug dosage enters the objective linearly. In this paper, pharmacokinetic equations (PK) which model the drug's plasma concentration and various pharmacodynamic models (PD) in terms of functions representing the concentration effects are included. It is shown that geometric properties of the PK and PD equations determine the qualitative properties of the optimal solution. Here for various models we analyze the local optimality of singular controls which correspond to treatment schedules with varying dosages at less than maximum rate.

Ulcerative mucositis has become an increasingly important toxicity of antineoplastic therapy. In an effort to establish mucositis risk prediction for specific cancer chemotherapy regimens, a 25 field database was developed. This paper describes the rationale and methodology for creation of the database and instructions for access to it via the Internet.

Dr. Roy Hertz is one of two scientists credited with discovering the first medical cure of a solid cancer. This paper presents a biographical history of Dr. Hertz and discusses his roles in the discovery of a cure for choriocarcinoma and as a pioneer for future research in cancer chemotherapy. This biography not only serves as a testament to the pioneering individuals in the field of chemotherapeutics but also represents the unique blend of medical, pharmacological, and physiological histories that led to the profound discovery. The timing and significance of the work of Drs. Hertz and Li cannot be overestimated. Their discovery was a spectacular success, demonstrating proof of the principle that chemotherapy can cure metastatic cancer and that an almost uniformly fatal cancer in young patients could be cured with a single chemotherapeutic agent, which stands as one of the greatest achievements in cancer research.

Mr. P. appreciated that newspaper more than all of the things that we were doing to preserve his life. The news that he got from the paper was stuff he understood—ERA, batting averages, standings, ...

Recently we proposed to use laser- and ultrasound-induced cavitation to enhance delivery of anti-cancer agents from blood into tumor cells through tumor capillary wall, interstitium, and cancer cell membrane. Cavitation threshold can be lowered by using microparticles (with certain optical and acoustic properties) which can accumulate in tumors after injection in blood. Lower cavitation threshold allows for local and pronounced cavitation in tumors and, therefore, may provide safe and efficient delivery of anti-cancer drugs in cancer cells without damage to normal tissues by laser or ultrasound radiation. In this paper, we studied enhanced penetration of model macromolecular (rhodamine-dextran) and real anti- cancer (5-FU) drugs and efficacy of cancer therapy with the use of this technique in nude mice bearing human colon tumors KM20. Our studies showed enhanced penetration of the drugs in irradiated tumors and significant improvement of cancer therapy when radiation was applied in combination with polystyrene particle and 5-FU injections. Complete tumor regression of irradiated tumors was obtained when optimum conditions were used. Our results suggest that this technique can potentially be used for efficient and safe cancer chemotherapy.

The main objectives of cancer treatment in general, and of cancer chemotherapy in particular, are to eradicate the tumour and to prolong the patient survival time. Traditionally, treatments are optimised with only one objective in mind. As a result of this, a particular patient may be treated in the wrong way if the decision about the most appropriate treatment objective was inadequate. To partially alleviate this problem, we show in this paper how the multi-objective approach to chemotherapy optimisation can be used. This approach provides the oncologist with versatile treatment strategies that can be applied in ambiguous cases. However, the conflicting nature of treatment objectives and the non-linearity of some of the constraints imposed on treatment schedules make it difficult to utilise traditional methods of multi-objective optimisation. Evolutionary Algorithms (EA), on the other hand, are often seen as the most suitable method for tackling the problems exhibiting such characteristics. Our present study proves this to be true and shows that EA are capable of finding solutions undetectable by other optimisation techniques.

In the present paper, the plant resources, chemistry, pharmacology and clinical therapy of berbamine are reported. From a botanical survey it was found that there are 32 species of Berberis plants containing berbamine utilized in China. Pharmacological studies demonstrated that berbamine possesses significant leukogenic effects in rats and dogs injured by an anticancer agent, and clinical observations demonstrated a therapeutic effect in 405 patients with leukopenia. It is suggested that berbamine may not only be useful as a leukogenic drug but is also useful as an adjuvant in cancer chemotherapy and radiotherapy.

Assessments of patient satisfaction have become widely accepted as a legitimate and worthwhile approach to improvement of service quality. Satisfaction studies are common in areas such as general practice or midwifery, but the approach has hardly been applied to assessments of care for persons with cancer. This paper first provides an historical background to satisfaction research in Western countries, then goes on to introduce conceptual issues in this field. Literature relevant to patient satisfaction with cancer chemotherapy services is then examined, with the review structured by four aspects of care: treatment accessibility and environment, technical aspects of care, interpersonal aspects of care, and patient information and education. The literature clearly suggests two areas which need urgent attention: assessment and management of adverse effects, and provision of patient information.

The multiresistant phenotype is an important problem in cancer chemotherapy. It is characterized by cell resistance to multiple and structurally unrelated drugs. We have shown previously that K562 multiresistant leukemia cells could be differentiated from their sensitive counterparts (wild-type K562 cells) on the basis of their infrared spectrum. In ATR FTIR mode, the penetration depth is controlled by both the wavelength and the incident angle, allowing depth profiling of samples. In this paper we took advantage of the ATR capability to modulate the penetration depth of the infrared wave into the cell, by modulating the incident angle, to investigate the differences between K562 multiresistant cells and their sensitive counterparts (wild-type K562 cells) as a function of this penetration depth. It is shown that focusing the IR beam on the most discriminant depth allows improvement of the discrimination between multiresistant and sensitive K562 cells. It is suggested that the depth profile of the difference spectra could allow a more precise localization of the biochemical modifications arising within the multidrug resistance phenotype.

Germ-line cytogenetic damage is well documented in laboratory animals exposed to anti-cancer agents, but has been harder to verify in the human. This paper reviews published studies demonstrating cytogenetic damage in human sperm following exposure to anti-cancer chemicals, as measured by the human-sperm/hamster-egg cytogenetic technique and fluorescence in situ hybridization. These two assays have provided important information on one step in the pathway leading to induced, transmissible germ line damage in the human. By way of introduction, a short review of the traditional human endpoints used to address the question of induced, transmissible genetic damage in human germ cells (mutation epidemiology) related to anti-cancer chemicals is presented.

Clinical studies of infusional chemotherapy at The Cancer Center of Boston began in 1980 at the New England Deaconess Hospital and from 1986 to the present have continued under the auspices of a network of free-standing ambulatory cancer treatment centers in Massachusetts. Over 50 peer-reviewed articles on infusional chemotherapy and 3 textbooks on the topic have been published by clinical investigators associated with The Center, including phase I, II, and III trials, multidrug infusion programs, and combined chemo/radiotherapy programs. Bringing together the total experience in this review provides a perspective to address those areas that have been inadequately explored and a framework for future development in the field. This paper represents a comprehensive synthesis and analysis of clinical infusional studies carried out at The Center to the present with the goal of achieving those objectives.

In this paper the authors study the effects of drug resistance on the optimal drug schedule of a single drug in cancer chemotherapy. The optimal schedule is analytically determined when the tumour grows exponentially and the drug has a linear killing action. Drug resistance enters into the model via an effectiveness term that decreases the cell loss as the accumulation of the drug grows. It is found that the optimal schedule is one where the drug is given at its maximum feasible dose from the onset, with some difference at the end of treatment depending on the relative speeds with which resistance develops.

There have been significant improvements in the diagnosis and treatment of malignant tumours. However, recent advances in cytostatic therapy are associated with several adverse side-effects which can impair the patient's well-being and may result in refusal or delay of treatment. Nausea and vomiting are perceived to be the most unpleasant of these side-effects. This paper addresses some of the issues involved in the improvement of these two distressing symptoms.

Etoposide has been introduced into cancer chemotherapy 15 years ago. A considerable amount of knowledge about the pharmacokinetics has accumulated since then, several reviews have been published recently, for a more detailed discussion of earlier studies in this field these publications should be consulted [1,2]. This paper will predominantly deal with pharmacokinetic aspects of schedules other than the “standard dose” IV treatments, e.g., locoregional application, dose reduction with impaired organ function, high dose treatment.