OELs from Low Dose Extrapolation – Part 1

We are going to stick with OELs at least for the next few weeks because they represent fully half of the information we need to estimate risk as Industrial Hygienists and Exposure Assessors.

We discussed risk-based occupational exposure limits (RBOELs) or the description of the putative quantitative risk at any OEL (Risk@OEL) as being at the very top of the OEL Hierarchy as described in detail in the March 2014 edition of the American Industrial Hygienist publication: The Synergist.

http://www.aihasynergist-digital.org/aihasynergist/201403#pg2.   Indeed, it is a low dose extrapolation of a non-threshold model to a putative lifetime risk of cancer of about 1 in 1000 that is leading NIOSH to put forth an REL of 0.1 ppmv for ethylene oxide as discussed in last week's blog.

The inescapable reality in today's world is that for anyone to describe or estimate the level of risk at any Occupational Exposure Limit (OEL), one must do low-dose extrapolation of a dose-response curve.   Anybody can fit a line (math model) to animal data which occurs at doses causing effects.  The truth is that we are not particularly interested in the relatively high level of exposure that causes an effect during toxicological testing of animals.   We are, however, intensely interested in the toxicological effects that might happen in people exposed to much lower doses.

There are good reasons for this situation.   We would love to say that our tests tell us the complete dose-response story for ALL rats for their entire lifetime (chronic).  However, for a lot of reasons, we can only test a limited number of animals and usually only for a limited portion of their lifetime.   Because of this restraint we typically have to “push” the dose to a point where something bad happens to the animals in a toxicological sense.   I have heard it said by the toxicological colleagues: “The purpose of a toxicology test is to find toxicity”.    Thus, in many or most repeat-dose studies the top dose is set high enough that it will cause an untoward health effect but just below the level that will kill the animal.   That has been called the maximum tolerated dose or MTD.   This defines the highest dose to be used in the study .  Besides defining the top of the dose range, it provides a critical piece of information; namely, the nature of the “bad thing” that happens from overexposure to this substances.   It could be as simple as local tissue response for a highly irritating material or it could be one of a partial list of effects listed below:

•         Central Nervous System Depression
•          Neuropathy
•          Organ (liver, kidney) damage
•          Chronic Obstructive Pulmonary Disease
•          Birth Defects in young from exposed mothers
•          Cancer

This provides the Hazard Identification piece for the substance; viz., the nature of the “bad thing” that happens during overexposure.

From the group of animals that gets the MTD the dose is reduced in the 3 or 4 groups getting progressively less of the material.   Often the lowest group is designed to render a No Observed Adverse Effect Level or NOEL or NOAEL.   We have shown in previous blogs that this is NOT the dose where nothing happens in the entire population of animals but only were nothing statistically happens in the tested group.   In reality it typically represents about a 10% level of frank adverse effect for the entire population if the entire populations were exposed to this level.

Lowering the dose for a group typically lowers the level of response.   This is the so-called dose-response relationship and is a touchstone of the science of toxicology.   It clearly shows cause and effect in a scientifically controlled experiment.  The cause is the dose and the effect is the general monotonically increasing level of response with increasing dose.

The standard manner of mathematically expressing these test results is what is known as quantal response.   Say we have 10 animals per group and 5 groups.   In the MTD or top group, none died in 2 years of testing but almost call got cancer of the liver, that is 9 out of 10 or 90%.   That is the quantal response; that is, they either got cancer or they did not. (The most dramatic example of quantal response is in acute lethality testing, where the animals typically either die or completely recover at any particular dose).   In the bottom or lowest dose group 1 out of 10 (10%) got liver cancer; however, this was not statistically different from the response of the unexposed controls.   Thus, the bottom group is the NOEL.   Note:  This is not a fanciful example but a situation that happens quite often in chronic tests of rodents.

So now we have spent quite a bit of money and have the following data to show for it; 90% cancer response in the top dose group, 10% dose in the bottom group and intermediate response in the 3 intermediate groups. 

As mentioned above, we are not particularly interested in a dose that may cause the lowest detectable (10%) cancer response in 10 rats but in the dose or exposure that presents a MUCH lower cancer risk in people.   We are forced to do low dose extrapolation.   As mentioned in the last blog, acceptable (or at least not unacceptable) putative cancer risk for workers has been set around 1 in 1,000 for the last 30-40 years in the modern era of quantitative risk assessment.   In the same vein, the putative cancer risk for folks in the general public is set much lower, ranging from 1 in 10,000 to 1 in 1,000,000.

How do we go from these high risk rat data to low risk extrapolated estimates for humans?   Many models will fit the actual rat data quite well but give VERY different predictions when extrapolated to low risk at low dose.    We will explore the details of this situation next week.

Dealing with LOW OELs

Recently a colleague wrote to me with the following comment:  “I was gnashing my teeth today over NIOSH's ethylene oxide REL of 0.1 ppm… Would you be willing to write a blog exploring this?”

My last blog discussed the new NIOSH Guidelines on setting OELs (at least the OELs for carcinogens) on the basis of putative risk and that the quantitative risk level would be set at exposure that renders approximately a 1 in 1000 lifetime risk of getting cancer from the exposure..  This is known as a Risk Based Occupational Exposure Limit (RBOEL) and represents the top of the OEL Hierarchy that was the topic of the lead article in this month’s American Industrial Hygiene Association SYNERGIST. 

I have not done so but apparently, when you run the numbers, the putative risk of cancer during a working lifetime exposure to 0.1 ppmv ethylene oxide is about 1 in 1000 and this RBOEL will be forwarded by NIOSH as their REL. 

Well this new REL is 10 fold lower than the current OSHA 8 hour average OEL of 1 ppmv and 5 fold lower than the current OSHA action level of 0.5 ppmv.   Indeed, ethylene oxide is a gas and I am quite sure there are some uses of this gas that are well controlled to the current OSHA limits but would not be controlled under the new REL.  

Does this mean that the old OEL is dangerous?  The answer is, not necessarily.  The old OEL could be safe even though when you run the standard  model for cancer risk, the risk at 0.5 ppmv would come out to be significantly higher than 1 in 1000.   An important question remains: Is this predicted risk real?  You may remember that any level of risk at these low concentrations including the level of estimated risk at this exposure of 0.5 ppmv is putative which according to the dictionary definition of putative means it is “generally thought to be or to exist, even if this may not really be true”[emphasis added].   The bottom line is that we do not have the scientific tools at this point to tell us that it is true; however, by using some assumptions it fits the above definition.   You should be aware that there remains a lot of uncertainty about the true risk at any of these exposure limits.

So how does one set any OEL, especially one that might have the strength of law?    OEL setting always has and always will boil down to a matter of politics.    On its face, if we set the OEL lower it means less risk to human health from exposure to this chemical.  Right?   Well, this is not necessarily true.   Some folks will argue that there is NO threshold for a cancer dose response especially for an alkylating agent like ethylene oxide; however, from my perspective that assertion has not been proven.   A concentration of 0.5 ppmv may be below a true threshold in humans.   I just do not think the science is there to prove otherwise.

Does that mean we should not lower the OEL given the current model and an appropriate sense of precaution?  Indeed, what if it is true?   Are we willing to allow this many cancers as a result of these workplace exposures?  The fact remains that this level of quantitative risk is what the chosen model is telling us albeit with assumptions and a considerably amount of uncertainty.   

The reality is that lowering this (and other carcinogenic OELs) appears to be where we are heading politically.  Also, it may be that epidemiological studies can and will back up the need for this change; however, the lower OELs will come at a cost.   Lower limits mean more controls and more resources dedicated to managing this putative risk.   Resources are, by definition, finite and if taken from one area have to come out of another.  It is a cost and some will probably bear the cost more than others but resources will have to be shifted and this could have untoward consequences even to health and safety.   I will not go into the details of it here but it has been fairly well demonstrated that being generally poorer as a society (less output of goods, less jobs, less taxes) could mean more risk to human health.

I am not skilled at politics or economics and I do not have the answers but I did promise to at least explore the issue.   My opinion is that if low OELs are truly problematic from an economic perspective all the elements of these changes need a holistic investigation of the entire spectrum of consequence to workers, the economy and the public health.   If overall it is deemed worthwhile using an open and democratic process, the the OELs should be lowered.

One real advantage of RBOELs for me is that one has a quantitative basis for the exposure limit and, if properly done, a description of the uncertainty associated with that limit.   For me that represents a significant increase in transparency over an OEL setting system based on safety factors born of expert judgment.

From a scientific perspective, I believe we ultimately have to get much better at understanding what really is happening at the human tissue level when workers are subjected to concentrations at current and recommended OELs.  Reduced uncertainty and more confidence will ultimately be cost-effect.  It will translate into spending money only where we really need to in order to reduce exposures and real risk while avoiding over-regulating and its inevitable waste.  

A look at a few dose-response models for carcinogens (in addition to the current linearized low-dose model) and how we might lower the uncertainty of the risk at the OEL will be the topic of a future blog.

Comments on NIOSH Update of Carcinogen Classifications and Risk Levels

NIOSH is proposing an update of their “Carcinogenic Classification and Target Risk Level Policy for Chemical Hazards in the Workplace”.  My friend and colleague Chris Laszcz-Davis asked that I comment on these changes so that she could forward comments to the American Industrial Hygiene Association.  I thought I would share these comments below on this blog.

The new classification policy proposes using the assessment schemes used by the NTP, EPA and IARC which makes plenty of sense because to do so will enhance harmonization and will keep NIOSH from reinventing the wheel. 

The use of risk based exposure limits (RBOEL) for carcinogens is a step directly into the 21st century for NIOSH.   The chosen benchmark of one in 1000 risk of cancer at the 95th lower confidence limit for a 45 year working lifetime seems imminently appropriate to me.   Mention is made in the document that this risk is at least an order of magnitude higher than the cancer risk permitted in the US for the general public.   What is not mentioned in the document is that, according to the Bureau of Labor Statistic, the risk for accidental death occurring during employment in a working lifetime in the United States is slightly higher than 1 in 1000 over the entire US worker population and very much higher for some classifications of workers (e.g., construction workers, commercial fisherman).  Note to viewers of TV show:  Deadliest Catch.  It really is perhaps the most dangerous occupation in the US.   It is certainly the most dangerous of the occupations that I know of that are tracked.

What is even more interesting is that these accidental deaths of workers represent actuarial data; that is, this the portion of working folks who actually died as evidenced by historical records.   The risk of cancer from exposure to a carcinogen on the other hand is putative and the result of low dose extrapolation of animal data.   The extrapolation also assumes that there is a linear dose-response all the way down to exposures that are many orders of magnitude below those tested on animals.   It also estimates the occurrence of cancer and not the rate of death from cancer.  

Low-dose extrapolation modeling will be the topic of at least one future blog but suffice it to say here that when you extrapolate with one model (linearity) far below the level of data you are essentially presenting an argument without data.

Given all these factors, the criterion outlined by NIOSH for RBOELs for carcinogens seems perfectly reasonably to me.

I also agree with the decision to make the RELs risk-based; that is, NIOSH will no longer consider the technical achievability (i.e., ability to control exposure) in establishing these limits.

I disagree with one area of the proposal; namely, the treatment of RELs set when the reliable analytical quantification limit is higher than an REL set using the above criteria (i.e., the 1 in 1000 quantitative risk level).   Here NIOSH is proposing using a higher REL with an AF notation for Analytical Feasibility.  This policy implicitly ignores the ability of modern exposure science to estimate exposures in essentially any scenario by physical-chemical modeling.    My suggestion would be to have two RELs in this instance.   The first would be the standard REL using the above criteria and the second an REL-AF to reflect the analytic realities. 

For many, perhaps most, carcinogens this will bring down the OEL significantly and this will undoubtedly present problems for some who need to comply with these new lower levels.   A colleague asked me to comment on this issue which I will do in the next blog.

1994 Contract with America: Lessons for Risk Assessment

This happened 20 years ago but I think this episode still provides food for thought today.   Back then I subscribed to a Business Publishers, Inc., Newsletter that was entitled "INDOOR POLLUTION NEWS".   I Googled it recently and seems to be gone; however, in one of their issues from around 1994 they  presented an editorial opinion on Risk Assessment (RA) that I think is still worth discussing in 2014 .  The piece is presented below:

"RISK ASSESSMENT - One Section of the Republicans Contract with

 America calls for regulatory agencies to use risk assessment, that

 is the scientific measurement of risks to be regulated-before rules

 are promulgated.  The focus on risk assessment by business and then

 made into a campaign issue by the Republicans seems clearly a

 subterfuge to gut environmental laws. ...  The science behind much of

 risk assessment is costly and in some cases problematic, rather than

 err on the side of caution for public and environmental health, the

 thrust now will be to negate rules and regulations that cannot be

 backed by ironclad science."

Wow!  This really struck me at the time because it was the strongest and most direct statement I have seen relative to the fears of the governmental, regulatory risk assessment/management community.   It got me to thinking about what I believe regarding this subject.   What I came up with long ago I still believe but with perhaps some of the more youthful naivete removed.  

My opinion is that RA is just a tool but it should be a tool that is used honestly.  If one uses the worst case then one should also present the "best" case and, if possible, the "average" case in a RA.  UNCERTAINTY born of a lack of basic knowledge makes worst cases worse while it broadens the error bands around the evaluation of risk in general.  Ideally and perhaps, in retrospect, naively in 1994, I saw the proposed legislation as asking for the honest reporting of risk knowledge and uncertainty.   Indeed, open reporting does not have to result in the “gutting” of environmental laws but it could allow for better decisions and better allocation of resources.  One-sided RA (only worst case) tends to hide behind the appearance of certainty.  Serious decisions need to have all the "cards on the table face-up" and all the uncertainty revealed in the most open and honest manner we are capable of. 

This blog has been discussing OELs of late.  There is plenty of uncertainty around the setting of OELs but the gist of the message so far has been that we need to do the best we can in rationally describing the process and for me that mean quantitative modeling predicting the range of risk at the OEL.   If the various models are very uncertain they are at least open relative to their particular quantitative assumptions.  It is this openness that can lead to identification of areas for improvement (spelled "research" and "money"); however, if we continue to cling to a somewhat hidden or ill-defined basis for setting OELs, then I am afraid that improvement and progress will come very slowly if at all.

Finally, it should be understood that the technical details and error bands around any RA only represent some of the “cards", we still have plenty of subjective political and cultural values that need to be acknowledge, openly faced, reported and factored into most decisions including OELs.  My vote is  that we take the onus off of technical RA to do everything and recognize it as a valuable tool that needs some nurturing (again that is spelled “research money”) if it is to really make good on its potential to render more light than heat regarding any question of risk.

Uncertainty/Safety Factors in OELs

From my perspective, most OELs are set these days by taking a No Observed Adverse Effect Level in a repeat dose toxicology study (NOAEL or NOEL) and dividing it by an uncertainty or safety factor (SF).  Last week’s blog was about NOELs.   This week it is about uncertainty/safety factors. 

As we discussed last week, NOELs are in reality typically frank effect levels for the adverse health effects from exposure.   The portion of any tested animal population having a frank ill effect from a NOEL exposure has been shown to range from 2-21% depending on the experimental design and shape of the dose-response curve.   Assuming people are as sensitive as rodents, an exposed population of 100,000 workers and a 2-21% response results in 2,000-21,000 workers whose health has been adversely effected by exposure to the NOEL.  Clearly, the NOEL would not make a good OEL and some adjustment needs to be made.  Thus, an uncertainty or safety factor is applied to bring the OEL down to be a fraction of the NOEL.

Various schemes have been forwarded to size these factors.  A paper is available online that does a reasonable job of providing the details and the references for the standard OEL/Safety Factor approach written in 2000 by Dr. Robert H. Ku, Ph.D.,CIH.:  http://www.safebridge.com/pdf_docs/overview_of_setting_oel.pdf

Here is an excerpt from that paper dealing specifically with safety factors:

The number and magnitude of these safety factors depend on the quality of the data. In general, some of these safety factors may include: (1) a factor from 1 to 10 for animal-to human (interspecies) extrapolation (if the NOEL is based on animal data), (2) a factor from 1 to 10 for human-to-human intraspecies variability in response, (3) a factor from 1 to 10 to consider study duration (a long-term study being more helpful than a short-term study), (4) a factor to consider the persistence of the drug in the body (or elimination half-life), and (5) a factor to accommodate for absorption efficiency by different routes of exposure.

Thus, it appears that the overall safety factor can range from close to 1 to a factor larger than 1000. In my experience, they typically come out to be significantly less than 100. 

As a range, I have seen safety factors as low as 3 for fully reversible, local tissue irritation response effects and greater than 1000 when considering highly uncertain data for a dreaded health effect (i.e., cancer).

From viewing it for many years, my sense is that the entire process is somewhat subjective and, in my estimation, to a certain degree driven by the practical ability to control the exposure at whatever limit is decided upon.

   

Indeed, one of the pioneers in our field, Dr. Stanley Roach along with his coauthor Dr. Stephen Rappaport published a paper on this subject in 1990.  The title of this paper and its reference:  But they are not thresholds: A critical analysis of the documentation of threshold limit values, American Journal of Industrial Medicine, Volume 17, Issue 6, pages 727–753, 1990.

Their primary observations and conclusion:

•       “Upon analysis it was found that, where the exposure was at or below the TLV, only a minority of studies showed no adverse effects (11 instances) and the remainder indicated that up to 100% of those exposed had been affected (8 instances of 100%)”

•       “…a surprisingly strong correlation was found between the TLVs and the (workplace) exposures reported in the corresponding studies cited in the Documentation [of the TLVs](emphasis added).”

Perhaps a new study should be done to see how much this situation has changed in the last 24 years.

That fact remains, however, that in most cases the size of the safety factors are driven by guidelines and are not subject to stringent rules.   We are often advised that this is necessary to allow for “professional judgment”.    I think we can all see how such a subjective system can potentially lead to somewhat unconscious bias in which the resulting OELs might become strongly correlated with workplace exposures as the study by Drs. Roach and Rappaport found.

In recent years there has been a scheme to strictly apply safety factors to toxicology data put forward by the REACh regulations in Europe.   This is done in the determination of the recently devised OEL class known as DNELs.  In the interest of time and space I am not going into the details of the DNELs and the safety factors here but suffice it to say that the process in general has cause a lot of consternation primarily because of its prescriptive nature.  

So where are we with all of this?   My sense is that the current system is clearly in need of some improvement.   The National Academy of Sciences seems to agree and has forwarded a rational modeling scheme for determining or estimating the amount of risk that exists at any exposure including the exposure limit for both carcinogens and non-carcinogens.   This is Chapter 5 of the so called “Silver Book” which you can download for free (You can download just Chapter 5 or the entire book) at:   http://www.nap.edu/download.php?record_id=12209

Even though this book has been out since 2009, the OEL setting community has not embraced the recommendations.   Some folks in our community including this blogger are asserting that it should now become an increasingly prominent topic of discussion within our world.