The approach of population pharmacokinetics uses covariates for dose individualisation of patients. Currently chronological age is used as a common covariate. However, chronological age may not be a good indicator of actual aging. For example a person of 90 years may be very healthy whereas a person of 60 years may be biologically much older and might be near his death. This intrigues us to think that measures of biological aging could be better used as a covariate in the context of population pharmacokinetic modelling for dose individualisation.
Pharmacokinetic (PK) models are simple mathematical schemes that represent complex physiological processes. Accurate PK modelling is important for precise determination of the model parameters where the individual parameters vary depending on what physiological process is being modelled.
Three main approaches to PK analysis are:
- Traditional compartmental models where the body is divided into different compartments e.g. routine compartmental modelling involves one, two and three compartments.
- Physiologic models where each organ and its function is considered e.g. liver (blood flow and volume) and liver metabolism (intrinsic activity)
- Linear systems (non-compartmental modelling) where the body is considered as a single compartment and the drug is instantaneously distributed throughout the body.
Models are desired for many reasons for example:
- To provide a simplified description of an observation
- To describe the time course of drug action
- To suggest appropriate doses and or dosing intervals
Population PK (POP PK) is an area of clinical pharmacology that uses PK models. POP PK aims to quantify typical PK parameters for drug absorption, distribution, metabolism and excretion termed ADME from a general population. POP PK can include between subject variability (BSV) and residual variability which means model misspecification error, assay error and unexplained error. POP PK models are then used for predicting individual PK parameters that are necessary for dosage individualization.
POP PK models find their most important use in direct patient care through dose individualisation avoiding side effects and increasing the efficacy of treatment.
The population approach to PK evaluation includes:
To provide PK data in special populations (e.g. elderly, children, renally impaired) and to support dosing recommendations for these populations. POP PK can provide typical mean values and knowing how the actual patients and speacial populations vary, you can predict individual values. This approach has been used frequently for studying drug clearance in both the elderly and in paediatrics.
2. Identification and measurement of sources of variability:
e.g., BSV, within subject variability (WSV) and between occasion variability (BOV).
3. Explain variability by identifying factors of demographic, pathophysiologic, environmental or drug related origin that influence PK behaviour. This is generally achieved using covariates. Examples of covariates include demographic characteristics like body weight, age and pathophysiological conditions like liver, kidney impairment
4. Quantitatively estimating the amount of unexplained variability in a patient population
Covariate modelling is an extension of population PK modelling where adding a covariate accounts for BSV for each parameter which helps improve the predictive performance of the model because there is less variability. Covariates improve the model because their usage reduces the unknown variability. Each covariate accounts for an amount of BSV. A covariate is any variable that is specific to an individual and may influence the PKs or pharmacodynamics (PDs) of a drug, e.g. weight, age and sex.
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Covariates can be classified as intrinsic (e.g. age, race, weight and height) or extrinsic (e.g. dose, compliance and co-medication). Intrinsic are present within individuals and are less able to be influenced than extrinsic. Extrinsic are present outside of the individual and may cause changes. The extrinsic covariates may be changed and may also be variable throughout the study. They can also be categorised as:
Nominal (non-ordered) e.g. race
Ordinal (ordered) e.g. renal impairment – mild, moderate and severe.
e.g. age, body weight, renal impairment defined by CrCL
Objectives of covariate modelling:
Covariates explain random variability in population pharmacokinetic analysis
Covariates help to understand sources of variability:
- This can be useful for improvement in clinical therapeutic use (dose adjustment in renal impairment)
- They improve predictive performance of the model as there is decreased variability
- This can be applied for subjects in the current data set or simulations can be performed for predictions for future
Criteria for covariate selection in population pharmacokinetic modelling
The covariate must have biological plausibility. There should be a causal association from current biological or medical knowledge. Biological plausibility can establish a cause-and-effect relationship between a biological factor and drug clearance or a PD effect. It is important to evaluate whether a proposed covariate explains the underlying variability between subjects.
The covariate should have mechanistic plausibility. It is sometimes interchangeable with biological plausibility. However, it is not just an association but a mechanism by which the covariate influences the PK or PD in the patient population.
The covariate should have statistical plausibility. When a covariate is added to a model, it should be statistically significant according to a prespecified criterion. Many statistical tests can be used to evaluate this e.g. Chi-square test, t-test, Wald test statistic or AIC (akaike information criteria) or BIC (Bayesian information criteria) values.
Clinical plausibility: The covariates should be clinically significant when included in the model. A covariate is generally considered clinically relevant if it accounts for 20% or more of variability.
Covariates are added into the model based on the relationship between the parameter of interest and the covariate information. The mathematical relationship may be linear, exponential or power function :
Linear: If the covariate and parameter relationship is linear. The example below shows a linear relationship between clearance and body weight.
e.g. CL = Ñ²1 + Ñ²2*Body weight
Exponential: The following example shows a exponential relationship between covariate body weight and clearance.
e.g. CL = Ñ²1*exp(Ñ²2*Body weight)
Power: The covariate body weight can be written as below to have a power relationship with the clearance
e.g. CL = Ñ²1* (Body weight) Ñ²2
Latent means present but not visible, apparent or actualized. In pathology, it means a disease remaining hidden or dormant. In the context of general health, latent means unobservable but can be measured using psychometric scales. For the purpose of this study, latent covariates are the covariates which are not available at the time of the clinical study. An example is genotype. We may not be sure of which genotype a subject belongs to unless we do genotype the DNA and classify them. Hints to genotype may be available from observing the phenotype, but genotype can only be timely determined by analysing DNA gene expression.
Aging can be defined as the decline in functional capacity that occurs over time. This functional disability increases the risk of death. Aging is heterogeneous and is different among different individuals and in different organs within a particular individual. Aging is not a disease but the risk of disease increases as a function of increasing age. Aging comprises of many interactive and interdependent processes of many molecular and cellular factors that determine life span and health. Some of the terms commonly used in the biology of aging and their definitions are included below:
Chronological age: The measure of time elapsed since a person’s birth (calendar age).
Biological age: The age determined by biology (both physiology and pathology) rather than chronology.
Physiological aging: Aging due to normal senescence of cells (senescence and genetic makeup).
Pathological aging: Aging due to pathological conditions (e.g. oxidative stress, poor nutrition, comorbidities, inflammation and environmental factors).
The rate of biological aging is different among individuals. Chronological age fails to provide an accurate indication of the biological aging process although it is an excellent indicator of how many years a person has lived. Biological aging is the impact on cellular and tissue functional capability. Therefore, it is easier to measure physiological age in organs or tissues in the hope of understanding the molecular basis of aging. Measuring how a tissue or organ changes with chronological age can identify biomarkers that can be used as a determinant of biological age. The biomarker can then be used to determine if an individual is biologically younger or older than his or her chronological age.
Theories of the biology of aging include:
Programmed theories (genetic theories)
Switching on and off of certain genes
e.g. SNPs in the human telomerase reverse transcriptase gene (hTERT) that are present in centenarians and these can be associated with longer telomere length
Biochemistry of the endocrine theory includes hormonal deficiencies, growth factors and heat shock proteins.
Error or damage theories (stochastic theories)
free radical theory: accumulation of oxidative stress
membrane hypothesis of aging
protein cross-linking and DNA repair and maintenance
In short, aging is the process of becoming older which is genetically determined and environmentally modulated and causes decline in the physiologic functions of various organs and systems.
Biology of the aging process
The oxidative stress, mitochondrial dysfunction, telomere shortening and genetic mechanisms form the core of aging perspective from cellular and molecular biological sciences.
The free radical aging theory was studied using various endogenous reactive oxygen species (ROS) which cause damage at the cellular level. ROS can be either superoxide and hydroxyl radicals or hydrogen peroxide and singlet oxygen which are activated forms of oxygen. The ROS are produced as a result of oxidative metabolism. There are anti-oxidant enzymes to counteract ROS like superoxide dismutase, catalase, glutathione peroxidise, glutathione transferases, peroxidises, and thiol-specific antioxidant enzymes. Apart from these enzymes, there exist several anti-oxidant substances like ascorbate, glutathione, beta-carotene and alpha-tocopherol. Aging is associated with undesired modifications to the molecular structure of DNA, proteins, lipids, and prostaglandins, all markers of oxidative stress. These molecular changes in proteins form the basis of cell aging and result in death.
Mitochondria and Aging
The mutations in the mitochondrial DNA by exposure to ROS generated within mitochondria lead to aging. Damage to mitochondrial DNA occurs because of age associated exposure to ROS which can cause aging and a decline in the activity of mitochondrial enzymes.
Aging is also associated with apoptosis. The process of programmed cell death is called as apoptosis. It is not clear whether apoptosis is caused by genetic factors. The stochastic free radical theory of aging may also cause apoptosis.
Genetic Mechanisms for Aging
Most of the longevity genetic variations in human beings are disease causing genes (apolipoprotein E4 allele) than genes of intrinsic aging process. The studies in centenarians reflect a genetic component to prolonged longevity. Genes causing progeroid syndromes have been identified e.g. Werner’s syndrome. Aging can be explained by altered gene expressions in the genetic sequence. The gene differences between centenarians and average-aged individuals may reveal aging processes and there are plenty of studies of the kind in the literature. The two very important genes associated with human longevity that have been replicated in many populations are FOXO3A and APOE. There are other candidate genes studied which includes APOC3, IGFIR and hTERT in centenarians.
Aging and pharmacokinetics
PK changes have been observed in older individuals, however these have been found to be variable if chronological age is the measure of aging.
Most studies found that gastric acid secretion decreases with the aging process. This can alter the ionisation of drugs and thus may alter PK behaviour. Aging reduces gastric emptying time, reduces peristalsis and decreases the colonic transit time. Passive intestinal permeability is not affected in the old but the active transport may be affected. Blood flow to the GIT in the elderly may be limited. This decreased blood flow may result in decreased systemic bioavailability. Alterations in intestinal drug metabolism with aging are unknown. However, hepatic phase I metabolism is reduced which causes reduction in drug clearance. There have been conflicting results regarding the PK and aging on drug absorption. Some studies have not shown age related change in absorption whereas some have shown significant changes in absorption of drugs. The discrepancy observed between studies may be due to difference in the method used for determining absorption. Aging increases the bioavailability of drugs undergoing extensive first pass metabolism or there can be reduced activation of prodrugs to active moiety.
Aging is associated with changes in body composition; elderly people have increased fat and decreased water content. So, polar drugs will have smaller volumes of distribution (Vd) in older people compared to adults e.g. gentamicin and digoxin. In contrast, nonpolar compounds have larger Vd with increasing age e.g. diazepam and thiopentone. Thus, loading doses may have to be changed in the elderly people.
Metabolism and elimination:
With increasing age, the functional capacity of many organs affecting drug clearance reduces. Kidney is primarily involved with excretion of water soluble drugs. Drugs like aminoglycoside antibiotics and dogoxin show decreased clearance in the elderly compared to adults. The glomerular filtration rate (GFR) decreases with increasing age and the Cockroft and Gault (CG) equation is used for dose adjustment of drugs in the elderly. This has been widely employed for narrow therapeutic range drugs like gentamicin, digoxin and lithium. GFR in the elderly may not change with age in the absence of disease. On an average there is assumed to be a decline of 1mL/min/year after 30 years, but GFR may not decrease at all in healthy elderly. In the Baltimore longitudinal study, one third of individuals showed no decrease in GFR measured for the 20 year period whereas the remainder of the population showed a distinct decline where this varied to great extent.
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The clearance of drugs eliminated by phase I metabolism in the liver is decreased in the elderly. There have been plenty of reviews published in regard to the aging and effect on hepatic drug clearance. It is believed that there has been a less blood flow to the liver of approximately 40% in old people compared to adults. The liver volume is also believed to reduce in the elderly. Impaired hepatic drug clearance is because of the age associated changes in hepatic blood flow and liver mass. There was no correlation between age and the cytochromes P450 enzyme activities from microsomes obtained from liver biopsies. There are no studies pertaining to the existence of common variants in drug-metabolizing enzyme genes. Aging whether affects the transporter proteins is not studied. There is a general belief that the clinical studies conducted to determine the influence of aging were poorly designed. Most of the studies included healthy elderly people failing to recruit the frail elderly.
The elderly population are usually on multiple drug therapy with multiple comorbidities. Age related physiological and pharmacokinetic changes as well as the presence of comorbidities and comedication will complicate drug therapy in the elderly. Metabolic drug clearance whether changes with age is of utmost importance because clearance is a determinant of dosing. Older people exhibit great variability in responses to medicines and this makes selection of dose difficult. Drug clearance is decreased consistently for high clearance drugs and there has been conflicting results for low clearance drugs. Total drug clearance reflects both the intrinsic clearance of free drug and the extent of protein binding. Highly protein bound drugs free clearance is changed. The Buttler & Begg hypothesized after an extensive review of literature that in elderly people, intrinsic metabolic drug clearance is impaired in the order of 20-60%. The clearance of flow limited drugs (high clearance) is reduced in elderly subjects in the order of 15-60%.
The Buttler Begg hypothesis
The clearance of total drugs is impaired for capacity limited (low clearance) drugs with low protein binding, since total drug clearance is a reflection of the intrinsic clearance. The clearance of free drug is impaired for capacity limited (low clearance drugs) with high protein binding. Measuring total concentrations is relevant for capacity limited drugs with low protein binding as this reflects intrinsic clearance for this class of drugs. While for highly protein bound drugs, this change might have been masked and so measuring free drug concentrations will reveal whether there is actually a decline in drug clearance. Buttler & Begg reviewed the literature for the studies which have been conducted regarding the elderly and drug clearance and they found that free clearance is consistently reduced in elderly people to less than Â½ of that in young. Glucoronidation (Phase II) is found to be less affected by aging than phase I metabolism. The literature examination for Ibuprofen PK suggested that stereoselective effect of aging suggests altered enzymatic activity than the reduced liver volume or blood flow.
They have summarised the evidence for decrease in drug clearance in elderly as follows:
A consistent decrease in clearance of 15-60% is seen in elderly people for high clearance drugs. There is 30-50% decrease in total clearance of capacity limited drugs with low protein binding. There is a decrease in free clearance of around 50% in elderly people of capacity limited drugs with high protein binding. However, though there is a decrease in drug clearance, there are no good biomarkers which can accurately reflect this decline. This is because age is an arbitrary value and may not give a good indication. Chronological vs functional, frail vs fit should be identified. Further, comorbidities, pharmacogenetics, comedication, different metabolic pathways influence drug clearance to a varied extent. Drug clearance decreases with age due to decrease in organ function, changes in body composition, decrease homeostatic reserve, comorbidities and comedication.
Chronological age (CA) is often used as a covariate. However, it is observed that drug clearance decreases with increasing age but that this is not consistent across the whole population. It has been found that people appear not to age at the same rate due to genetic makeup and environmental interaction. Chronological age does not account for either pathology or physiology. However, biological aging is difficult to measure. Hence, the concept of biological age has developed. The use of biological age as a covariate may has the potential to greatly improve POP PK estimates. It appears biological aging combines physiological processes (genetic) and pathological processes (environment, nutrition, diseases, oxidative stress and pathology). As far as the drugs which are renally eliminated, there exist easily available biomarkers like creatinine clearance which can aid in drug dosing. Estimated creatinine clearance though with some drawbacks, can solve the purpose for renal drugs. However, there is a concern for liver metabolised drugs for which there are currently no good biomarkers exist which can help in drug dosing. The Buttler and Begg clearly hypothesized that there is a decline of the free drug clearance for the liver metabolised drugs in the elderly. This requires dose adjustment in elderly patients. But, studying age associated changes in the liver is not without problems and the results may be not interpretable if the study is not designed properly. So, the biomarker value may be missed if it is studied directly for liver drugs. Hence, initially we intend to study these biomarkers for the renal drugs for which the age associated changes in the drug clearance have been studied extensively and well documented for this class of drugs. Further, our intention to use gentamicin as a probe drug can overcome the drawbacks of creatinine clearance, a kidney function marker. Gentamicin clearance is well correlated with creatinine clearance.
Biomarkers of aging
A biomarker is a characteristic that is measured and evaluated as an indicator of normal biologic or pathogenic processes or pharmaceutical responses to a therapeutic intervention. A biomarker is defined as a parameter of interest correlating with a certain outcome of a biological process independently of being part of it. Biomarkers of aging indicate specifically the biological age or physiological changes, caused by the process of aging of cells, tissues, organs and organisms. Baker and Sprott stated that biomarkers of aging are “biological parameter(s) of an organism that either alone or in some multivariate composite will, in the absence of disease, better predict functional capability at some late age than will chronological age”. In the context of biological aging, telomere length is the most often cited biomarker. These biomarkers are viewed as important as they determine the quality of organs and tissues. This is particularly important from our perspective as the quality of organs determines their functioning which will have an impact on drug clearance.
Measurement of aging
Biomarkers of age and aging are important for many reasons. Aging is not defined and it is unclear. It proceeds at different rates in different individuals. If everyone “aged” at the same rate and same pattern, chronological age would have served the purpose of measuring. But, aging is associated with individuality. Chronological age provides an approximation of declining functional capacity time until death. Two individuals of the same CA, one can be severely deteriorated and near death while the other is in robust good health.
Desired characteristics of biomarkers of aging:
- It should change with time at a rate that describes the rate of aging
- It should indicate physiological age
- It should explain some basic biologic process
- It should be needed for the maintenance of health
- It should serve as a prospective as well as a retrospective aging marker
- It should be reproducible
- It should display change over a relatively short period
- It should preferably be non-invasive and should be some endogenous component
Issues of Biomarkers measurement:
- Validity: The concern is whether the biomarker is actually measuring aging.
- Reliability: Is the biomarker repeatedly measured with less error and also with less intra individual variability.
There have been difficulties in conceptualizing and in using biomarkers of aging. It may be unwise to think that there exist single biomarkers which can serve for all aging study purposes.
The word telomere is derived from the Greek words telos and meros which mean “end” and “part”. Barbara McClintock proposed that telomeres protected chromosome ends from recombination and allowed them to be distinguished from double strand breaks. Leonard Hayflick observed in-vitro that human cells in tissue culture stop replicating after a certain number of cell divisions by a process known as replicative senescence. He proposed that the in-vitro cell senescence phenomenon could be very useful to study human aging at the molecular and cellular level. Human cells can proliferate repeatedly during the lifetime but not indefinitely. Alexei Olovnikov attributed the limited number of cell divisions reported from in-vitro studies by Hayflick to replication of telomeres. He identified human somatic cells might not rectify the telomere shortening that occurs when cells replicate repeatedly. Many studies reported a loss in average telomere length with repeated cell divisions over age in somatic cells of the blood. These observations led to the conclusion that somatic cells are unable to maintain telomere length. However, germ line cells can maintain their telomere length. Telomeres are present at the ends of linear chromosomes. They consist of thousands of repeated non-coding DNA sequences of ‘TTAGGG’
Functions of telomeres
Protect chromosome ends from recombination or degradation by the enzyme nuclease. They allow the cell to differentiate between natural chromosome ends and damaged DNA. They provide a means for the mechanism for replication of linear DNA ends. So it can be said that telomeres stabilize chromosomes and preserve genetic information. During each cell division, the chromosomes are replicated without any problem. But, telomeres are not fully replicated. This is due to the problems with DNA replication i.e. end replication problem. Also, telomere attrition has been caused by other problems like oxidative stress and chronic inflammation. When telomeres become critically short, cellular senescence is initiated.
End replication problem
The biochemistry of DNA replication is associated with the end-replication problem. During DNA replication, one strand runs 5′-3′ while the complimentary strand runs 3′-5′. One of the primary reasons for the telomere shortening is the end replication problem. If we look at the DNA replication, it does not begin at either end of the DNA strand, but begins in the middle of the DNA. The DNA polymerases can move in the 5′ to 3′ direction only. This results in a leading and a lagging strand on the replicating DNA strand. The DNA polymerase can form a complementary DNA strand on the leading strand easily because it moves from 5′ to 3′ and there is no issue. However, there is an issue on the lagging strand since DNA polymerase can not move from 3′ to 5′. The little RNA sequences act as primers and attach to the lagging strand at a distance from the initiation site. The Okazaki fragments are thus formed. More RNA primers and DNA polymerase act simultaneously to form a new DNA strand. The last RNA primer attaches, and DNA polymerase, RNA nuclease, and DNA ligase act to convert the RNA to DNA and the gaps between the Okazaki fragments are sealed. But, to change RNA to DNA, there must be a DNA strand in front of the RNA primer. This occurs at all the sites of the lagging strand, but it does not occur at the last RNA primer. This RNA is destroyed by enzymes that degrade any RNA left on the DNA. Thus, a section of the telomere is lost during each cell division at the 5′ end of the lagging strand. So, after repeated rounds of DNA replication, the telomeres will finally get smaller.
Telomere length is a result of cumulative oxidative and inflammatory stress of the life, as well as oestrogen exposure. There is evidence that telomere length is associated with lifespan and it can be considered a biomarker of aging. There are studies showing association between leukocyte telomere length and many of the common diseases of aging, lifestyle factors and socioeconomic status. Manipulation of genetic control of telomerase activity controlling telomere length may have clinical applications. Telomere shortening may also be increased by inflammatory processes, where cell turnover rate is increased. The shorter telomere length can be associated with surrogate markers of inflammation such as IL-6 and CRP.
Telomere loss is most rapid during early life, when about 1 kb of the telomere is lost per year due to high proliferation initially. Later, environmental influences resulting in oxidative or inflammatory stress contribute to varying telomere attrition rate. Thus telomere length is shortened with increasing age. There is also a gender difference in telomere length, in that length in females is often longer than that of males. Since there is no significant gender difference in telomere length at birth, the observed longer telomere length in females may be related to the estrogen associated increase in telomerase activity via the hTERT gene expression. Further, estrogen is an excellent anti-oxidant agent. Gender difference in telomere length explains the greater life expectancy in women compared with men.27-29
Studies of telomere length in humans are of interest in various aspects of the aging process. Telomere dynamics allow the balance between cell senescence and immortalization to be examined: cell senescence results in limited lifespan, while the latter to the development of cancer. Studies of how various environmental factors affect telomere length will help to understand lifespan and health outcomes throughout the life. Lifespan can be correlated with kb of telomere length. It may be a reflection of cumulative exposure to oxidative and inflammatory stress throughout the life course and therefore associated with frailty. Frailty is physical or psychological loss of function and is determined by frailty index.
Telomere length is maintained by a protein-RNA complex, telomerase, which includes
- TERC region
- TERT region
Telomere length declines per year leading to a loss of 21 bp per year. There is an association between paternal age and telomere length of children and can be explained from the observation that sperm telomere length increases with age. Replicative cell senescence would affect functioning of tissues and organs and leads to the aging process. There are limited studies which have been undertaken to study age associated telomere shortening within the same individual. These studies have shown telomere loss with age. Cross-sectional studies in general show a negative association between age and telomere length, but there is wide variation in the results and also not consistent. Survival is shorter among those with shorter telomere length in a study conducted in the elderly. A negative correlation between age and telomere length has also been observed in men aged 65 years and older. However, no association was found in other studies of older people aged 85 years and older. It is possible that, at extreme old age, there are many confounding factors relating to selective survival accounting for the lack of association. The gender difference in life expectancy of approximately 7-8 years and the observation of longer telomeres in females compared with males provide strong proof for the role of telomere length in lifespan.
Telomere length measurement
Several methods are available for telomere length measurement including southern blotting, flow-FISH and qPCR technique. The first two methods are tedious. The q
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