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Optimizing Antiplatelet Therapy, and Drug Therapy in General. What is wrong with the Pharmaceutical
I would like to relieve myself of some really pent-up feelings about the pharmaceutical
industry and its behavior.
I have gone to the PAGE meetings for years, and the ACoP and ISoP and the Am. College of
Clinical Pharmacology, and I have become more and more concerned about the domination of these
groups and Clinical Pharmacology in general by the pharmaceutical industry in ways that have become
very disturbing to me. I quit the ASCPT years ago because of this.
All the time they make models, and often very good ones from a scientific point of view.
The industry does very good basic science.
But nothing seems to happen after these models are made. There is so little discussion of
the medical and social use of this work. I would say that over 99% of this work never gets used
after being presented at meetings such as those above. All is modeling and simulation. There is
essentially no idea of CONTROL – of what can actually be DONE clinically with these models. After
all the science, the MARKETERS get hold of it.
For example, Clopidogrel and Prasugrel are marketed basically as “one dose fits all” [1,2]. Studies
conclude that prasugrel has greater antiplatelet effect and better cardiac outcome than clopidogrel.
However, the significant role of dosage was ignored. Results were presented simply as being due to
the drugs. More information could have been obtained had investigators used a population
pharmacokinetic – dynamic model of each drug, including serum concentrations (if available) as well
as antiplatelet effect. Such population models may exist.
Great interpatient variation was seen in antiplatelet effect with both drugs. In both the
Clopidogrel and Prasugrel groups, the degree of antiplatelet inhibition ranged from 70% remaining to
none remaining. The role of the dosage regimen in the two groups was not discussed at all, and this
is the case in almost all such studies. No one pays any attention to the dosage regimen after it has
been decided upon by the company marketing the drug. It becomes the dose for everyone.
The studies [1,2] might, and could, I believe, have compared each patient’s measured effect with
clinical outcome, to determine the antiplatelet effect best separating good and adverse outcomes, to
provide a target for future individualized therapy. Initial dosage regimens could then be developed,
individualized for each patient’s age, gender, height, weight, etc., to achieve the desired
antiplatelet effect with maximum precision (minimum expected weighted squared error) [3,4]. One can
monitor effect and adjust dosage optimally, especially using nonparametric population models,
multiple model dosage design, and Bayesian adaptive control software.
Here is the gist of a letter I sent to the An. J. Cardiol
THE LETTER - I read with great interest the article by Migliorini et al, entitled ”Comparison of the
Degree of Platelet aggregation Inhibition with Prasugrel versus Clopidogrel and Clinical Outcomes in
Patients with unprotected Left Main Disease treated with Everolimus-eluting Stents”, which appeared
in 2013; 112:1843-1848. The paper concludes that “prasugrel is associated with increased inhibition
of platelet aggregation and with a better clinical outcome than is clopidogrel”.
I would like to raise the following points for the authors’ comments. The study is stated to be
sponsored by the investigators. How, why, and by whom was this particular study design developed?
Why did prasugrel have the greater antiplatelet effect? Is it possible that the observed differences
in antiplatelet effect were the result of the specific dosage regimens of each drug that were given
in that study?
Further, how can one use the data obtained to optimize antiplatelet therapy? There was great
variation in the observed antiplatelet effect in each of the two patient groups. It ranged from 70&
remaining activity ) small response to the drugs) to no remaining antiplatelet effect at al.( see
their Figure 2.
. It would be useful to compare the antiplatelet effect observed in each individual patient with
that patent’s final clinical outcomes. One could then link the effect responses with the various
clinical outcomes. One could then have found a specific value of antiplatelet effect which separates
the various good and bad outcomes optimally, and which can then serve as an optimal therapeutic
target of antiplatelet effect to be achieved for each new patient in the future.
In future clinical use, initial dosage regimens can then be developed, individualized for each
patient to important clinical covariates such as age, gender, height, weight, etc., to achieve the
desired target goal with maximum precision (minimum expected weighted squared error) [1-6]. Each
future patient can then have his antiplatelet effect monitored, and the dosage adjusted
appropriately, especially using nonparametric drug models, multiple model (MM) maximally precise
dosage design, and Bayesian adaptive control, to most closely achieve this target value. In this way
each individual patient can be positioned to maximum advantage during his/her course of antiplatelet
therapy. One can avoid the “one size fits all” approach to therapy currently described.
It is a pity that the pharmaceutical industry, the FDA, and the wider medical community appear not
to have taken advantage of the opportunities and tools described in the references here, to
facilitate such an approach.
The pharmaceutical industry, FDA, and medical community should consider the opportunities of
individualized, maximally precise, optimal stochastic Bayesian adaptive control. If the mechanism of
drug action can be measured, it might be best to use such targeted approaches to optimize
individualized therapy, rather than “one dose fits all”, where no patient knows if he/she is well
positioned for effect or not.
THE AUTHORS REPLIED that the study was sponsored by the investigators, but they did not answer the
specific question of who designed the study.
They stated that studies by their group have shown that “poor responsiveness to clopidogrel is a
strong independent predictor of cardiac death and stent thrombosis in patients receiving
drug-eluting stent, including patients treated for unprotected left main disease. In this high-risk
subset of patients, high residual platelet reactivity after 600 mg clopidogrel was the only
predictor of cardiac death and stent thrombosis”. Nevertheless, the deeper platelet aggregation
inhibition achieved with prasugrel was associated with an 80% reduction of the composite of cardiac
death and myocardial infarction at 1 year without increase in major bleeding. They agreed that “it
would be good to avoid the “one size fits all” approach to antiplatelet therapy, but a “true
individualized therapy also using genomics or the models suggested is very far from reality,
considering the variations in each individual patient of platelet aggregation induced by many
variables (surgery, bleeding, physical activity, acute coronary syndrome, and others) or
spontaneously over time.” David Antonucci MD, Angela Migliorini MD, Renato Valenti MD, Florence,
Italy, 17 March 2014
Of course, it is such variability they are talking about that needs to be minimized by the
well-known techniques of nonparametric population modeling and Bayesian individualization of the
models, and maximally precise multiple model (MM) dosage individualization.
PROGRESS, BUT- Whenever dosage regimens have been individualized, mortality has decreased, care
improved, hospital stay shortened, and costs per patient admission decreased. However, the silent
but determined opposition of the pharmaceutical industry, the stifling inertia of the medical and
pharmacy schools, and the apparent total lack of interest on the part of the FDA, all provide
ominous obstacles. They have failed to keep up with the advances made in pharmacokinetics and its
clinical applications over the last 60 years. A great social and psychological change needs to be
made. Physicians must be trained in this, but there is hardly anyone to teach them, as almost no one
with clinical expertise is trained for it.
THE CURRENT SITUATION - The pharmaceutical industry preys on these untrained and pharmacokinetically
illiterate physicians and markets its drugs, it seems to me, only for short term profit, without
thinking of what is optimal for the patient. It is almost as if they think that they themselves will
never get sick and require such therapy! Look at their advertising for Clopidogrel and Prasugrel.
They specifically say that the drug DOES NOT NEED TO BE MONITORED! WHAT IS WRONG WITH THE INDISTRY
AND THE FDA?The cost of dosage individualization today appears to be borne mainly by the hospital
laboratory in measuring the serum concentrations, and by the pharmacy in providing pharmacokinetic
consulting for dosage individualization in the few centers where it is used. The hospital
administrators appear not to be aware that the extra expense borne by them is greatly offset by the
shortened hospital stay and significantly reduced cost associated with the patients’ shorter stay.
WHAT NEEDS TO BE DONE - Currently available clinical software provides the tools for optimal,
maximally precise, dosage individualization. It is available at www.lapk.org.
1. Dosing services need to be set up in hospitals to implement current techniques for optimal
individualized drug dosage, for aminoglycosides, vancomycin, antifungals, HIV drugs, transplant
drugs, antiepileptics, and many others.
2. Medical and Pharmacy schools can teach maximally precise individualized drug therapy using such
software, providing optimal patient care and “math and pharmacokinetics without tears”.
We have a long way to go and much to do!
All the very best to all,
1. Antonucci D, Migliorini A,, and Valenti R. Reply. Am. J. Cardiol. 2014;113:
2. Migliorini et al.: Comparison of the Degree of Platelet Aggregation Inhibition with
Prasugrel versus Clopidogrel and Clinical Outcomes in Patients with unprotected Left Main Disease
Treated with Everolimus-eluting Stents. Am. J. Cardiol. 2013;112:1843-1848.
3. Wiviott et al.: Prasugrel versus clopidogrel in Patients with Acute Coronary
Syndromes. N. Engl. J Med. 357:
4. Bustad A, Terziivanov D, Leary R, Port R, Schumitzky A, and Jelliffe R: Parametric
and Nonparametric Population Methods: Their Comparative Performance in Analysing a Clinical Data Set
and Two Monte Carlo Simulation Studies. Clin. Pharmacokinet., 45: 365-383, 2006.
5. Bayard D, Milman M, and Schumitzky A: Design of Dosage Regimens: A Multiple Model
Stochastic Approach. Int. J. Biomed. Comput. 36: 103-115, 1994.
Roger W. Jelliffe, M.D., F.C.P., F.A.A.C.P.
Professor of Medicine Emeritus,
Founder and Director Emeritus
Laboratory of Applied Pharmacokinetics
USC School of Medicine
Consultant in Infectious Diseases,
Children’s Hospital of Los Angeles
4650 Sunset Blvd, MS 51
Los Angeles CA 90027
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It was timely someone of founder-fathers (as Bob correctly pointed out) of clinical population PK-PD
theory and practice to summarize the status quo of cumulated issues in drug R&D. According to my
humble opinion the simplicity you are talking about arises from some deficiencies in under- and
post-graduate education in pharmacology and clinical pharmacology of under-and post-graduate medical
students and physicians. Medical education is getting more and more expensive and savings are often
made from inappropriate segments.
As to the role of competent authorities, I would say that, unlike to academic community, their
degree of freedom to error is very, very limited. That’s why universities should offer really
trustworthy and validated data.
All the very best,
Dimiter Terziivanov, MD,PhD,DSc, Professor
Head, Dept. of Pharmacology and Clinical Pharmacology
“ST. KLIMENT OHRIDSKI”
FACULTY OF MEDICINE
UNIV HOSP "LOZENETZ"
1 Koziak str.
1407 Sofia, BULGARIA
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The tailoring dose to a patient's specific physiology, metabolism and disease state should be a
priority not only to improve efficiency but to limit side effects. The high cost of therapies
should promote the efficient application as a cost saving, health improving strategy. Some
secondary companies are in fact working to provide patient genetic profiles to understand
probability of effective therapy and to reduce or limit adverse side effects but those are just
starting and a wider application to tailoring dose is coming.
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Thanks for your note. Genetic profiles are seductive, but they usually have not been
incorporated into quantitative approaches to dosing - rather just another factoid to remember. Also,
they are not always correct. A colleague of mine had an HIV patient with somnolence from her HIV
drug, and elevated serum concs and impaired clearance, as might well be suspected from a SNP. Her
problem was addressed with our software and individualized dosing, and she did very well thereafter,
with a clear head and undetectable viral load. When tested for the SNP, she did not have it.
That is one problem with the genetic testing. The other problem is that there are still so many
unknown SNPS, and all the drug interactions one can think of, and there is NO way to address all
these except by TDM itself, and maximally precise Bayesian adaptive control of dosage to hit desired
targets, using nonparametric models and maximally precise multiple model design of dosage regimens.
That process is the only one I know which specifically takes into account ALL SNPs and all
interactions. Control is control, period. The software tools are there now. They simply need to be
used, and not by adding extra expense such as the incomplete information (at best) provided by
genetic testing without incorporation into good population PK models as covariates.
You are right. When these approaches have been used, care is better, mortality down, hospital
stay shorter, costs are less.
1. Jelliffe R, Schumitzky A, Bayard D, Leary R, Botnen A, Van Guilder M, Bustad A, and Neely M:
Human Genetic variation, Population Pharmacokinetic – Dynamic Models, Bayesian feedback control, and
Maximally precise Individualized drug dosage regimens. Current Pharmacogenomics and Personalized
Medicine, 7: 249-262, 2009.
3. Schumitzky A: Nonparametric EM Algorithms for Estimating Prior Distributions. App. Math. and
Computation. 45: 143-157, 1991.
4. Leary, R., Jelliffe R., Schumitzky, A., and Van Guilder, M An adaptive grid non-parametric
approach to pharmacokinetic and dynamic(PK/PD) population models, 14-th IEEE Symposium on Computer
Based Medical Systems, 389-394, 2001.
5. Bustad A, Terziivanov D, Leary R, Port R, Schumitzky A, and Jelliffe R: Parametric and
Nonparametric Population Methods: Their Comparative Performance in Analysing a Clinical Data Set and
Two Monte Carlo Simulation Studies. Clin. Pharmacokinet., 45: 365-383, 2006.
6. Jelliffe R, Bayard D, Milman M, Van Guilder M, and Schumitzky A: Achieving Target Goals most
Precisely using Nonparametric Compartmental Models and "Multiple Model" Design of Dosage Regimens.
Therap. Drug Monit. 22: 346-353, 2000.
All the best,
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