Cumulative Responses to Two Unknown Chemicals - Research Paper Example

Task/Question 2.  Plot, analyse and interpret the following tabulated information using information you have encountered in the module activities. Table 1: Cumulative dose-response curve of two unknown chemicals under standard testing procedures, lethality experiment.   Cumulative Response (%) Dose (mg kg-1) Chemical A Chemical B Chemicals A + B 5 0 6 6 10 4 15 20 15 17 45 25 20 50 67 38 25 86 70 49 30 98 73 56 35 100 75 61 “All substances are poisons: there is none which is not a poison. The right dose differentiates a poison and a remedy.” Paracelsus (1493-1541) Each data point in the graph represents the percent mortality of the three drug doses. The lowest experimental dose where there is no measurable effect is known as the No Observable Effect Level (or NOEL). this is the concentration that the experimenters failed to see any mortality. From the graph we can see that chemical A has NOEL at drug dose of 10mg/kg. At this dose concentration chemicals B & combined administered A & B show a cumulative response of 20% i.e 20 % mortality. NOEL is a useful measurement for extrapolating risk and safe exposure concentrations. The slope of the linear transformations of the lines can be obtained from the equations given below in the format y=mx+c Potency The slopes are chemical A> chemical B> chemical A+B Thus we can infer that chemical A is more potent than chemical B is more potent than combined dose of A & B Maximal Efficacy Again Maximal efficacy of chemical A is 100%, chemical B is close to 80%, A+B is close to 60% Thus maximal efficacy of A is highest and A+B is lowest. LD50 calculation from log transformed probit scale The LD50, or statistically-derived dose that is lethal to 50% of a modeled population, is a helpful tool to compare the toxicity of different compounds, or between different population models. Since the data is linear here, we can extrapolate a line across from the 50% response on the y-axis, to the corresponding concentration on the X-axis. Here from the y axis 50% of cumulative response corresponds to a dose of 50 mg/kg. Thus the Lethal Dose 50 (LD50) for chemical A is 20 mg/kg Similarly for Chemical B, 50% on the y-axis corresponds to a dose of 20mg/kg on the X axis. Thus the LD 50 for chemical B is 20mg/kg The 50% Y axis in combination corresponds to a dose of approximatel 26-27 mg/kg on the X axis. Thus combination of chemicals A&B has a higher LD 50 dose as compared to individual A & B of 27 mg/kg Further more, from the graph the equation for each curve can be obtained and the desired does for a required cumulative response or vice versa can be determined by substituting in equation of line: Chemical Equation of Line A y = 3.978x - 28.85 B y = 2.485x + 0.428 A+B y = 1.864x - 0.857 Where, y= Cumulative Dose Response x= Dose (mg/kg) Task/Question 3. (AAP)  Epidemiologists use tools to analyse disease data. The simplest measures are those of frequency (i.e.counts of disease). More complex measures include measures of association. By referring to both simple measures and measures of association, explain why both are useful in studying disease. Illustrate your answer with actual examples of their use that your have found through researching sources. Make sure you include a reference to each source used at the end of your answer. Various mathematical and statistical measures are used to analyze and draw trends of epidemiological data. These being with prelimnary measures of Central Tendency or measures of locating the Center of the data. These include Mean, Median & Mode. Mean is the average of the data. For example, the average rate of incidence of a particular disease per unit of population size. Median is the positional centre of the data values. When the individual values in a data are arranged in an ascending/descending (ordered) format, the middle value corresponds to the median. For example, Both Mean and Median are sensitive to outliers. Mode is the observation which occurs with the largest frequency in a data set. A data distribution can be unimodal or bimodal. For example Cases of influenza typically reach a peak in the winter months which can be represented by modal peaks. Range gives an idea of the spread of data and quartiles & percentiles divides the data into equal blocks. An example is the range of birth weights and correlation of low birth weight with incidence of diabetes or related metabolic disorders earlier on. Measures of disease frequency are used to describe how common an illness (or other health event) is with reference to the size of the population (the population at risk) and a measure of time. There are two main measures of disease frequency: Prevalence & Incidence. Prevalence measures existing cases of disease and is expressed as a proportion. For example: At admission, 150 patients were screened for the presence of breathlessness, and 30 resulted affected by the symptom. This results in breathlessness prevalence at admission of 20% (30/150). (Barratt & Kirwan 2009) Prevalence can be measured as Point Prevalence at a single point of time and Period Prevalence which measures disease during a given period interval. Incidence is a measure of the number of new cases of a disease or other health outcome that develop in a population of individuals at risk, during a specified time period. For example: At admission, 120 patients free from breathlessness are followed for 3 weeks. In this period of time, 45 developed breathlessness. This results in a incidence rate of 45 per 120 (or 37.5 %) during the 3 week period. This means that, at admission, each patient had a .375 probability to develop breathlessness during the next 3 weeks. (Barratt & Kirwan 2009) Data on incidence are useful in health service planning. It is important to know the number of new cases of disease which will arise in a population in the future to be able to plan services required and how to deal with them. For example, in India, approximately 2.6 million cataract operations are done each year. In spite of this huge effort, the number of people blind with cataract in the country is increasing.(Evans 1997) As well as providing information on disease burden in the community, epidemiologists are also interested in whether different groups have different risks of developing disease. This can be useful for health service planning and can also help in the process of finding out the causes of disease or aetiology. Another method of measuring incidence is to calculate the odds of disease. Instead of the number of individuals who are disease-free at the start of the study, odds are calculated using the number disease-free at the end of the time period. Age/sex specific mortality rate, birth rate, fertility rate, mortality rate, case fatality rate % are some other measures of disease frequency used in epidemiology. Measures of Association A major goal of epidemiology is to establish a cause-effect relation between factors and occurrence of disease. However due to the observational nature of epidemiologic studies, the observed association may very well due to chance or personal bias or might be confounding to previously established studies. Also an absence of association does not necessarily imply the absence of a causal relationship. The Bradford-Hill criteria are widely used in epidemiology as a framework with which to assess whether an observed association is likely to be causal.(Higgins & Constantini) These criteria will be illustrated with the help of SV40 association with some human mesothelioma cancers as causative which is obtained in toto from SV40 Cancer foundation website. (Horwin 2003) 1. Strength of the association - According to Hill, the stronger the association between a risk factor and outcome, the more likely the relationship is thought to be causal. Eg: Several studies have found evidence of SV40 in ~50% of human mesotheliomas, both in the United States and in some European countries 2. Consistency of findings - Have the same findings been observed among different populations, in different study designs and different times? Eg: Several studies have found evidence of SV40 in ~50% of human mesotheliomas, both in the United States and in some European countries 3. Specificity of the association - One to one relationship between cause and outcome. Eg: SV40 is present in a highly specific group of human tumours. Furthermore, injection of the virus into hamsters produces the same tumours. In human mesotheliomas, SV40 is found only in tumour cells and not in the surrounding non-malignant tissue 4. Temporal sequence of association - Exposure must precede outcome  Eg: Little is known, although the virus has been found in preneoplastic lesions. The tremendous increase in the incidence of mesotheliomas over the past several decades was preceded by the administration of SV40-contaminated poliovirus vaccines, as well as by an increased exposure to asbestos 5. Biological gradient - Change in disease rates with corresponding changes in exposure (dose response). Unknown. Because humans are permissive to SV40, millions of SV40 particles are produced from few infected cells 6. Biological plausibility - Presence of a potential biological mechanism. SV40 is a powerful carcinogen that, in vitro, transforms human mesothelial cells. It induces mesothelioma development in hamsters. SV40 also expresses the oncogenic protein T Ag in human mesotheliomas, which inactivates p53 and RB. Definitive epidemiological studies are lacking 7. Coherence - Does the relationship agree with the current knowledge of the natural history/biology of the disease? The association does not conflict with what is known about the natural history of mesothelioma, and it might explain why those with no history of asbestos exposure can develop the disease 8. Experiment - Does the removal of the exposure alter the frequency of the outcome? In vitro and in vivo evidence indicates a causal role for SV40 in mesothelioma development 9. Analogy. Other DNA tumour viruses can induce cancers in humans, and SV40 induces mesotheliomas in animals Ten years after Bradford Hills classic paper, Rothman presented a model of causation that stressed the multifactorial pathogenesis of disease, with multiple component causes or factors that increase risk, and diverse causal pathways. He identified necessary elements and combinations of exposures sufficient to result in disease development. Causal inference, then, would focus more on how well the results of epidemiological studies fit with such a model. Rothman and Greenland note that none of Bradford Hills criteria alone is sufficient to establish causality – for each criterion there are situations in which both lack of satisfaction of the criterion may be causal and satisfaction of the criterion may be non-causal. Temporality, the requirement that the exposure must precede the effect, is the only necessary criterion for a causal relationship between an exposure and an outcome. (Evans 1997) References: Barratt H. Kirwan M. 2009. “Epidemiology: Association & Causation”. Department of Healtk, UK. [online] Available at: http://www.healthknowledge.org.uk/parta/paper1knowledge/1_researchmethods/1a_Epidemiology/1a2_3_5_10.asp Higginson I. Constantini M. “Epidemiological Methods of Symptoms in Advanced Disease”, Symptom Research: Methods and opportunities. National Institutes of Health [Online] Available at: http://symptomresearch.nih.gov/chapter_19/sec3/cihs3pg1.htm Evans J. 1997.”Epidemiology in practice: Disease Incidence” Community Eye Health Journal, UK. [Online] Available at http://www.cehjournal.org/0953-6833/10/jceh_10_24_060.html Horwin M. 2003. “Bradford Hill Criteria and Mesothelioma” SV40 Cancer Foundation [Online] Available at: http://www.sv40foundation.org/Bradford-Hill.html Hill AB.1965. “ The environment and disease; association or causation?” Proc R Soc Med. Robyn M. Anthony J. 2005. “Association or causation: evaluating links between environment and disease. Bull World Health Organ [online]. Available at: Read More