Cellular models for disease exploring and drug screening

The biopharmaceutical industry has been greatly promoted by the application of drug and disease models, including both animal and cellular models. In particular, the emergence of induced pluripotent stem cells (iPSC) makes it possible to create a large number of disease-specific cells in vitro. This review introduces the most widely applied models and their specialties.     

Two models for screening chelating agents for cadmium removal

The effectiveness of some chelating agents to mobilize cadmium from Chinese hamster ovary cells after chronic exposure (20 hr), as well as from cytosolic metallothionein, was studied. In the first protocol, the most effective substance was 2,3-dimercaptopropanol, followed by 2,3-dimercaptopropane-1-sulfonate and 2,3-dimercaptosuccinic acid, whereas CaNa₃3-diethylenetriamine pentaacetic acid × 5H₂O showed less effect. Simultaneous incubation of cells with cadmium and the chelating agent resulted in a different order of effectiveness: CaNa₃ DTPA prevented cadmium uptake almost totally, 2,3-mercaptopropanol by 75% and 2,3-dimercaptopropane-1-sulfonate by 35%. Neither CaNa₃-diethylenetriamine pentaacetic acid × 5H₂O nor 2,3-dimercaptosuccinic acid had altered the distribution of cadmium between the cytosolic protein fractions after a 2 hr incubation of cells, whereas after this period, 2,3-dimercaptopropanol had removed all cadmium from metallothionein, and 2,3-dimercaptopropane-1-sulfonate about 50%. None of the chelating agents had reduced the amount of Cd bound to high molecular weight proteins. In the cell free system, 2,3-dimercaptopropanol and 2,3-dimercaptopropane-1-sulfonate were equally effective and removed all cadmium from metallothionein within ten minutes. CaNa₃-diethylenetriamine pentaacetic acid × 5H₂O, however, even after 60 min, had removed only 50% of the cadmium. The remaining cadmium was found distributed to the high molecular weight and lower molecular weight protein fractions.Abbreviations BAL 2,3-dimercaptopropanol - CHO Chinese hamster ovary cells - DMPS 2,3-dimercaptopropane-1-sulfonate - DMSA 2,3-dimercaptosuccinic acid - DTPA CaNa₃-diethylenetriaminepentaacetic acid × 5 H₂O - HMW proteins high molecular weight proteins - MT metallothionein     

Two-stage models for the analysis of cancer screening data

Methods are proposed for the analysis of the natural history of disease from screening data when it cannot be assumed that untreated preclinical disease always progresses to clinical disease. The methodology is based on a two-stage model for preclinical disease in which stage 1 lesions may or may not progress to stage 2, but all stage 2 lesions progress to clinical disease. The focus is on joint estimation of the total preclinical duration and the sensitivity of the screening test. A partial likelihood is proposed for the analysis of prospectively collected screening data, and an analogous conditional likelihood is proposed for retrospective data. Some special cases for the joint sojourn distribution of the two stages are considered, including the independent model and limiting models where the duration of stage 2 is short relative to stage 1. The methods are applied to a case-control study of cervical cancer screening in Northeast Scotland.     

Two criteria for evaluating risk prediction models

Summary We propose and study two criteria to assess the usefulness of models that predict risk of disease incidence for screening and prevention, or the usefulness of prognostic models for management following disease diagnosis. The first criterion, the proportion of cases followed PCF (q) , is the proportion of individuals who will develop disease who are included in the proportion q of individuals in the population at highest risk. The second criterion is the proportion needed to follow‐up, PNF (p) , namely the proportion of the general population at highest risk that one needs to follow in order that a proportion p of those destined to become cases will be followed. PCF (q) assesses the effectiveness of a program that follows 100q % of the population at highest risk. PNF (p) assess the feasibility of covering 100p % of cases by indicating how much of the population at highest risk must be followed. We show the relationship of those two criteria to the Lorenz curve and its inverse, and present distribution theory for estimates of PCF and PNF. We develop new methods, based on influence functions, for inference for a single risk model, and also for comparing the PCFs and PNFs of two risk models, both of which were evaluated in the same validation data.     

Quasi-likelihood estimation for relative risk regression models

For a prospective randomized clinical trial with two groups, the relative risk can be used as a measure of treatment effect and is directly interpretable as the ratio of success probabilities in the new treatment group versus the placebo group. For a prospective study with many covariates and a binary outcome (success or failure), relative risk regression may be of interest. If we model the log of the success probability as a linear function of covariates, the regression coefficients are log-relative risks. However, using such a log-linear model with a Bernoulli likelihood can lead to convergence problems in the Newton-Raphson algorithm. This is likely to occur when the success probabilities are close to one. A constrained likelihood method proposed by Wacholder (1986, American Journal of Epidemiology 123, 174-184), also has convergence problems. We propose a quasi-likelihood method of moments technique in which we naively assume the Bernoulli outcome is Poisson, with the mean (success probability) following a log-linear model. We use the Poisson maximum likelihood equations to estimate the regression coefficients without constraints. Using method of moment ideas, one can show that the estimates using the Poisson likelihood will be consistent and asymptotically normal. We apply these methods to a double-blinded randomized trial in primary biliary cirrhosis of the liver (Markus et al., 1989, New England Journal of Medicine 320, 1709-1713).     

Organ-on-a-chip technology and microfluidic whole-body models for pharmacokinetic drug toxicity screening

Microscale cell culture platforms better mimic the in vivo cellular microenvironment than conventional, macroscale systems. Microscale cultures therefore elicit a more authentic response from cultured cells, enabling physiologically realistic in vitro tissue models to be constructed. The fabrication of interconnecting microchambers and microchannels allows drug absorption, distribution, metabolism and elimination to be simulated, and enables precise manipulation of fluid flow to replicate blood circulation. Complex, multi-organ interactions can be investigated using “organ-on-a-chip” toxicology screens. By reproducing the dynamics of multi-organ interaction, the dynamics of various diseases and drug activities can be studied in mechanistic detail. In this review, we summarize the current status of technologies related to pharmacokinetic-based drug toxicity testing, and the use of microtechnology for reproducing the interaction between multiple organs.     

Cancer screening behaviors among smokers and non-smokers

Objective: We explored whether smoking is associated with cancer screening behaviors. Methods: We used data from the 2007 Florida Behavioral Risk Factor Surveillance System and the Florida Tobacco Callback Survey to examine screening behaviors related to four cancer types (breast, cervical, prostate, and colorectal). Using multiple logistic regression analyses, we examined the association between smoking status and health screening behaviors. Results: For 10 of the 11 cancer screening variables, being a current smoker was significantly associated with being less likely to ever have been screened and also less likely to be compliant with screening guidelines. For breast and cervical cancer, level of nicotine dependence was also significantly related to compliance with screening recommendations; women with higher levels of dependence were less likely to be compliant. Conclusions: Our results support the notion that individuals’ actions related to their health are consistent across different types of behaviors. We found that smokers were less likely to engage in cancer screening behaviors. In addition, among smokers, individuals with greater nicotine dependence had lower compliance with some screening tests. Physicians should ensure that their patients who smoke are receiving appropriate and adequate screening for cancer.     

Designing optimal allocations for cancer screening using queuing network models

Cancer is one of the leading causes of death, but mortality can be reduced by detecting tumors earlier so that treatment is initiated at a less aggressive stage. The tradeoff between costs associated with screening and its benefit makes the decision of whom to screen and when a challenge. To enable comparisons across screening strategies for any cancer type, we demonstrate a mathematical modeling platform based on the theory of queuing networks designed for quantifying the benefits of screening strategies. Our methodology can be used to design optimal screening protocols and to estimate their benefits for specific patient populations. Our method is amenable to exact analysis, thus circumventing the need for simulations, and is capable of exactly quantifying outcomes given variability in the age of diagnosis, rate of progression, and screening sensitivity and intervention outcomes. We demonstrate the power of this methodology by applying it to data from the Surveillance, Epidemiology and End Results (SEER) program. Our approach estimates the benefits that various novel screening programs would confer to different patient populations, thus enabling us to formulate an optimal screening allocation and quantify its potential effects for any cancer type and intervention.     

Vascularized microfluidic organ-chips for drug screening,disease models and tissue engineering

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Semi-continuous scale-down models for clone and operating parameter screening in perfusion bioreactors

Perfusion cell culture, confined traditionally to the production of fragile molecules, is currently gaining broader attention in the biomanufacturing of therapeutic proteins. The development of these processes is made difficult by the limited availability of appropriate scale-down models. This is due to the continuous operation that requires complex control and cell retention capacity. For example, the determination of an optimal perfusion and bleed rate for continuous cell culture is often performed in scale-down bioreactors and requires a substantial amount of time and effort. To increase the experimental throughput and decrease the required workload, a semi-continuous procedure, referred to as the VCD_max (viable cell density) approach, has been developed on the basis of shake tubes (ST) and deepwell plates (96-DWP). Its effectiveness has been demonstrated for 12 different CHO-K1-SV cell lines expressing an IgG1. Further, its reliability has been investigated through proper comparisons with perfusion runs in lab-scale bioreactors. It was found that the volumetric productivity and the CSPR_min (cell specific perfusion rate) determined using the ST and 96-DWP models were successfully (mostly within the experimental error) confirmed in lab-scale bioreactors, which then covered a significant scale-up from the half milliliter to the liter scale. These scale-down models are very useful to design and scale-up optimal bioreactor operating conditions as well as screening for different media and cell lines.     

Incorporating non-genetic risk factors and behavioural modifications into risk prediction models for colorectal cancer

Background: Epidemiological studies have identified potentially modifiable risks for colorectal cancer, including alcohol intake, diet and a sedentary lifestyle. Modelling these environmental factors alongside genetic risk is critical in obtaining accurate estimates of disease risk and improving our understanding of behavioural modifications. Methods: 14 independent single nucleotide polymorphisms identified though GWAS studies and reported on by the international consortium COGENT were used to model genetic disease risk at a population level. Six well validated environmental risks were selected for modelling together with the genetic risk factors (alcohol intake; smoking; exercise levels; BMI; fibre intake and consumption of red and processed meat). Through a simulation study using risk modelling software, we assessed the potential impact of behavioural modifications on disease risk. Results: Modelling the genetic data alone leads to 24% of the population being classified as reduced risk; 60% average risk; 10% elevated risk and 6% high risk for colorectal cancer. Adding alcohol consumption to the model reduced the elevated and high risk categories to 9% and 5% respectively. The simulation study suggests that a substantial proportion of individuals could reduce their disease risk profile by altering their behaviour, including reclassification of over 62% of heavy drinkers. Conclusion: Modelling lifestyle factors alongside genetic risk can provide useful strategies to select individuals for screening for colorectal cancer risk. Impact: Quantifying the impact of moderating behaviour, particularly related to alcohol intake and obesity levels, is beneficial for informing health campaigns and tailoring prevention strategies.     

On criteria for evaluating models of absolute risk

Absolute risk is the probability that an individual who is free of a given disease at an initial age, a, will develop that disease in the subsequent interval (a, t]. Absolute risk is reduced by mortality from competing risks. Models of absolute risk that depend on covariates have been used to design intervention studies, to counsel patients regarding their risks of disease and to inform clinical decisions, such as whether or not to take tamoxifen to prevent breast cancer. Several general criteria have been used to evaluate models of absolute risk, including how well the model predicts the observed numbers of events in subsets of the population ("calibration"), and "discriminatory power," measured by the concordance statistic. In this paper we review some general criteria and develop specific loss function-based criteria for two applications, namely whether or not to screen a population to select subjects for further evaluation or treatment and whether or not to use a preventive intervention that has both beneficial and adverse effects. We find that high discriminatory power is much more crucial in the screening application than in the preventive intervention application. These examples indicate that the usefulness of a general criterion such as concordance depends on the application, and that using specific loss functions can lead to more appropriate assessments.     

Alternative strategies of fetal sex diagnoses and sex preselection

Abstract

Motives for sex control include avoidance of sex‐linked disease and realization of preferred sex compositions of children. Currently, the only wholly effective means of sex control is diagnosis of fetal sex by mid‐trimester karyotyping of amniotic fluid cells followed by corrective abortion when diagnosis is adverse. Unfortunately the delays involved in karyotyping mean that abortion cannot be minimum‐risk suction curretage. Radioimmunoassay procedures allow somewhat earlier diagnosis and therefore less risky abortion, but entail more diagnostic error. In the first part of the paper, several assay procedures are evaluated in terms of relative expense as compared to karyotyping, gestational age when reliability is highest, and level of that reliability. Later portions of the paper focus on use of radioimmunoassay to diagnose fetal sex for purposes of regulating the sex composition of offspring. Three strategies are compared with respect to their efficiency and expected levels of diagnosis and abortion.     

Cancer screening in older adults.

Adults aged 65 and older represent an increasingly important segment of the US population. Cancer is an important cause of death in this group. Screening for cancer can significantly reduce cancer incidence and mortality. In this review I address screening for breast, cervical, prostate, lung, colorectal, and ovarian cancer in older Americans. Decisions about screening for cancer must consider the effects of screening, diagnostic evaluations, and treatments on the quality of life of each person.