The race-ethnicity confusion

Definitions of race and/or ethnicity are spectacularly confused and lack any kind of precision whatsoever.

The FDA in its guidance, for example, make a distinction between race and ethnicity, but allow them to be combined in a one step self declaration.

Race refers to 5 categories:
American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, White
Ethnicity has 2 categories, either Hispanic/Latino or not Hispanic/Latino

Such definitions do not have any scientific logic and were actually developed by the Office of Manpower and Budget.

The major problem for us is that because this guidance is used by pharmaceutical companies in drug development studies, we cannot hope to have any meaningful ethnicity data that we can use. The only data are those lumped under a generic "Asian" umbrella, which essentially denies the vast ethnic diversity in our communities.

Singapore is not any wiser and contributes to the confusion by ambiguously mixing ideas of race, ethnicity and even language into the definition. We identify 4 major categories from census definitions: Chinese, Malay, Indians and Others. The categories of Chinese, Malays and Indians are defined using fairly circuitous logic about origins which also incorporates language/dialectual characteristics. To make matters worse, race/ethnicity based medical studies often rely heavily on hospital records which for citizens are almost entirely based on recorded information from the National Registration Identification Cards. The information here do not use the same definitions as the census, but are almost completely self declarations according to self perceptions.

Race or ethnicity?

Race and ethnicity are terms which are often used interchangeably. To a large part, I believe it is due to the increasing political incorrectness of using race as a descriptor, thus moving the notion of race more towards that of ethnicity.

A 1950 UNESCO declaration "The Race Question", succinctly puts it:

"National, religious, geographic, linguistic and cultural groups do not necessarily coincide with racial groups: and the cultural traits of such groups have no demonstrated genetic connection with racial traits. Because serious errors of this kind are habitually committed when the term 'race' is used in popular parlance, it would be better when speaking of human races to drop the term 'race' altogether and speak of 'ethnic groups'."

Race in its original intent referred to the "roots" of population groups (etymology: Germanic 'reiza' - bloodline). This to a large extent referred to biological characteristics and the external appearance of the population groups. The term 'race' therefore carries heavy connotations about biology and heredity.

We know now that these notions are fallacious, and believe that there are no biological bases for classification of peoples into racial groups. Race is little more than a socio-political construct that allow societies to manage resource allocations.

The UNESCO declaration was correct. The term 'race' should actually be dropped from our vocabulary altogether as it is meaningless from a biological perspective, and its continued use just continues to perpetuate the myth that people can be sub-classified according to biology.

The more correct and useful term is 'ethnicity'. Ethnicity refers to population groups that are unified by social and cultural characteristics as well as geographic origins. That different ethnic groups have differing phenotypic features do not make them definable by these characteristics.

Pharmacogenetic studies as well as studies looking at inter-populational variability in drug responses, should refer to ethnicity as a descriptor of the population rather than to race. This would then include allele frequencies, anthropometric information, environmental exposures and other societal pressures. The academic challenge presented is how do we standardize these ethnicity descriptors so that that we can understand and apply the data being generated from these studies. Correctly however, ethnicity data have limited applicability across populations. For example, data on Chinese in Singapore do not necessarily apply to Chinese populations in China, Africa, Europe or US. Likewise, we should not too readily apply Chinese data generated in Beijing to a Singapore therapeutic context.

In our lab we use the terms Chinese, Malay and Indians because these are terms used by our National Registration Office. However in our studies we recruit subjects based on self-declaration of ethnicity (Chinese, Malay and Indians) consistent over three generations. The data we generate will be correct for our population as at this time, and we make no assertions that they can be representative of Chinese, Malay and Indian populations across the globe, or even for Singapore population groups for later generations.

Biomarkers of efficacy

Worthwhile pulling out for a read. Useful perspectives re approach for development of biomarkers for efficacy and disease progression.

The Pharmacogenomics Journal , (8 December 2009)

A multistep validation process of biomarkers for preclinical drug development
W M Freeman, G V Bixler, R M Brucklacher, C-M Lin, K M Patel, H D VanGuilder, K F LaNoue, S R Kimball, A J Barber, D A Antonetti, T W Gardner and S K Bronson

Abstract
Biomarkers that can be measured in preclinical models in a high-throughput, reproducible manner offer the potential to increase the speed and efficacy of drug development. Development of therapeutic agents for many conditions is hampered by the limited number of validated preclinical biomarkers available to gauge pharmacoefficacy and disease progression, but the validation process for preclinical biomarkers has received limited attention. This report defines a five-step preclinical biomarker validation process and applies the process to a case study of diabetic retinopathy. By showing that a gene expression panel is highly reproducible, coincides with disease manifestation, accurately classifies individual animals and identifies animals treated with a known therapeutic agent, a biomarker panel can be considered validated. This particular biomarker panel consisting of 14 genes (C1inh, C1s, Carhsp1, Chi3l1, Gat3, Gbp2, Hspb1, Icam1, Jak3, Kcne2, Lama5, Lgals3, Nppa, Timp1) can be used in diabetic retinopathy pharmacotherapeutic research, and the biomarker development process outlined here is applicable to drug development efforts for other diseases.

Direct, surrogates or outcomes?

Students are quite often confused during discussions of these various types of efficacy measures. It is really not surprising, as many clinicians I discuss with also seem quite unclear about these concepts.

But these are to me quite important ideas; ideas which are quite often overlooked during drug development and the design of therapeutic regiments. And we do pay a price for neglecting them.

When we move from the bench to the bedside, we do lose the ability to assess drug response. Often we do not even begin to recognize just how much we have lost in our ability to do this. Yet it is vitally important for us to be able to do this because if we cannot, we will not be able to rationally manage our dosage regiments. This is one of the great difficulties in managing therapeutics.

In a relative small subset of therapeutic situations we do have direct measurements of drug effect - such as in the management of hyper/hypotension or hyper/hypoglycaemia. The blood pressure and blood sugar responses serve us well. A similar possibility exists with the management of the INR using warfarin.

In many therapeutic situations, no clear biological marker of drug response exists; or the therapeutic aims is actually far more complex compared to the immediate aspects of drug response. In these situations the desired drug response may actually be an 'outcome' measure - such as the control of epilepsy or arrhythmia, or even the management of depression and psychoses. In such situations, a surrogate for drug action should be available that can allow real time management of drug dosages. In some situations, measuring drug concentrations, can provide you with a reasonable surrogate for managing dosages. This is referred to as having a 'target concentration strategy', or more popularly called therapeutic drug monitoring.

Admittedly, good surrogate measures are often not even available. This deficiency creates a therapeutic environment where 'therapeutists' are often limited to relatively fixed, or inflexible dosage regiments, and therefore cannot deal effectively with any patient variability in drug response. This could be as comforting as having your pilot fly blind.

The recent problems with rofecoxib (Vioxx) provides an interesting example. While initially developed for the management of inflammatory joint disease, the selective Cox2 inhibitor found a new use in the prevention of intestinal polyps. When used as an anti-inflammatory analgesic, rheumatologists could manage drug dosages through assessing pain relief, joint involvement etc. When it came to the prevention of intestinal polyps, the therapeutist essentially had to 'fly blind' using fixed dose regiments, since there was neither direct nor surrogate measures of drug action. Intestinal polyposis was at best an outcome measure that could only be assessed at the end of treatment periods. Were there patients who over over-dosed or under-dosed? Very likely. Could we have better managed the dosages, and consequently the risks of cardiovascular mortality? Quite likely; but we will never know now. Rofecoxib was eventually withdrawn from the market.

A pity, perhaps.

Further thoughts about distributions.....

For the most part, the volume of distribution Vd is conceptualized as little more than a proportioning factor between the administered dose of a drug and the initial plasma concentrations. While it was important in determining the C0, it had little impact on drug exposure, the AUC or steady state concentrations. These were squarely in the domain of clearance mechanisms.

We expected that while protein binding was inversely related to the Vd, the free drug concentrations would eventually equilibrate across all tissue compartments, regardless of tissue binding. What was observed in the plasma of the central compartment would represent what was happening in all tissues. Furthermore if free drug clearance remained unchanged, free concentrations would remain unchanged, regardless of protein or tissue binding.

According to these ideas, central compartment pharmacokinetics was of prime importance in understanding drug efficacy, or lack of it.

In recent years, the increasing appreciation that few molecules actually permeate across membranes with the involvement of transporter processes, have led many to question the wisdom of an approach that assumed drug molecules existed in equilibrium across membranes and consequently, tissue compartments. Depending on the expression and function of transporters at various membranes, drug concentrations can vary independently of central compartment concentrations. Hence, in some individuals, the plasma concentrations may mirror concentrations in any tissue compartments if the transporters involved are ubiquitous.

Conversely, in some individuals, the concentrations in tissues 'compartments' may be very different if specific transporters are differentially expressed, leading to widely different efficacy-toxicity profiles even though central compartment concentrations appear invariate.

Do changes in Vd have any impact on clinical drug effects?

This will depend entirely on which drug concentration measurements have the greatest impact on drug effects. And here is where our relative ignorance of this particular aspect of drug pharmacology constrains our ability to use PK as a predictor of drug effects.

If for example our understanding of what best predicts drug effects is limited to considerations of 'average' or 'steady-state' drug concentrations, then we are compelled to expect that Vd changes will have little or negligible impact on clinical drug effects. This is because Vd changes do not alter the area-under-the-curve (AUC) of a drug's pharmacokinetics (since AUC is determined primarily by clearance and bioavailability). Remember (F x Dose)/AUC = Clearance?

This line of reasoning has dominated and shaped our thinking for the last few decades as evidenced by the large number of studies looking at clearance or AUC changes as predictors of drug effects. Correspondingly, we have tended to minimize the effect of distributional changes as a predictor.

Is this line of reasoning correct?

Only to a limited extent. We have seen in the previous post, that the immediate and most obvious effect of Vd changes is on the C0. Consequently if a drug's effect is dependent on peak concentrations, Vd changes will have inversely related effects on peak concentrations of the drug profile, and by extension, any clinical effect, be it efficacy or toxicity, associated with the peak.

We see this also with multiple dose regiments.

With a multiple dose regiment, even though changes in Vd are not expected to change the AUC or steady state concentrations, you can see that the fluctuations over the dosage interval are greater if the Vd is smaller (in the above case 115L compared to 230L). With the increased fluctuations, one should note that the peak and trough concentration actually move in different directions, i.e. with a smaller Vd, the peak increases but the trough decreases, while the AUC remains unchanged.

Going back to the original question....do Vd changes have an impact on clinical drug effect? It will depend on whether drug effects relate best to steady-state concentration, the AUC, peak or trough concentrations. And this is poorly understood at the moment.

For the moment however, being over-focused on the AUC therefore limits our ability to see potential causes of variability in drug response.

Even more problematic ideas of distribution.....

The simplest idea of the volume of distribution (Vd) of a drug, is to think of it as the volume that a drug distributes into when that drug is administered into the body, but before any elimination has taken place. The simplest approach therefore is to divide the dose of the drug (intravenously administered) by the observed plasma concentration at zero time, C0, i.e. before any elimination has had a chance to occur.

Vd = Dose/C0

Not all of any drug, however, is uniformly distributed throughout the body; and what is seen in the plasma only represents a fraction of all of the drug molecules distributed throughout the body. If more is distributed outside of the plasma 'compartment', the C0 will be smaller for a given dose of drug, and the Vd will appear correspondingly higher. The converse is also true, that if less is distributed outside of the plasma compartment, the Vd will appear lower.

The original ideas of the process of distribution, was that drug molecules were mostly freely permeable entities, and found a 'distributional equilibrium' across cell and tissue membranes. Binding to proteins or other large molecules prevented their effective permeation across membranes, and therefore 'trapped' these drug molecules into various 'compartments'.

Conceptually therefore, the Vd of a drug may be seen to be 'governed' by an expression relating body volumes (presumably determined by body weight), plasma protein binding and tissue binding:

Vd = Volume of central compartment + Volume of peripheral tissue x (fu/fut),

where central compartment referred to plasma volume, tissue referred to undefined number of tissues outside of plasma, fu is unbound or free fraction of drug in plasma, and fut is the unbound fraction of drug in the tissues.

By this expression one can see that the Vd can be reasonably expected to be related directly to the extent of tissue binding, and inversely related to the extent of plasma protein binding.

The Vd of a drug can also be seen to have direct and immediate effects on the starting concentrations of any drug administered, particularly if administered intravenously. And if the starting concentrations were responsible for efficacy or toxicity, the Vd may be expected to be a major determinant of efficacy or toxicity.

Knowing the Vd also helps us estimate the starting dose of a drug if we have a target drug concentration in mind. For this reason, dosage regiments of drugs with large Vds often incorporate a loading dose regiment before settling into a lower maintenance dose regiment.