Too many drugs? Too few patients?

The article by Gina Kolata in the New York Times ( August 13, 2017) is written from  a perspective that favors drug companies over the patients.    

The current system of clinical trials  started when the average American lifespan was less than 70 years for men, before we knew anything about the molecular basis of cancer, and the slide rule was the most sophisticated, widely available, computational tool. It has always been clear that this system slowed drug availability and, consequently, led to deaths.  

From the patient's (and the caring provider's) perspective anything that improves the chance of more effective therapy is welcome. "Too many drugs" is when there are adequate treatments for all diseases.  "Too few patients " is universal wellness...at reasonable cost. 

The article quotes two Genetech executives (see same day NYT page 7 for the ad for an unrelated Genentech product)  questioning how many immunotherapeutic  agents can be tested.  Genentech's entry in this field,  TECENTRIQ®,,is having trouble proving its worth .  The company  may want to beat its competitors.  The irony of a Merck vice president saying: "How many  PD-1 antibodies does Planet earth need? " is not lost on me.  He might well think that the Merck product, Keytruda, is enough. We do not know that .

The idea of "outstrip[ping] our progress in understanding the basic underlying science"  is incredibly arrogant.  Most of our currently effective  cancer treatments  were developed in the context of models that have been long abandoned.   Our current understanding of immunology and  gene interactions remains primitive and will almost certainly be changed in the future.   Part of refining the model  will depend on the results of treatment with these medicines.  That is the history of medicine. 

In the article the "cost" of genetic testing is quoted at $5000. I find that interesting .  The cost depends on who is paying.  Currently, the materials cost is less than $100.  The amount billed to the insurance company is often about $8500.  If the patient pays directly, it is no more than $3500.

From the  cancer  victim's perspective, the problem is not too many drugs.  The  problem is  the cancer.  The drug companies, the  FDA  and insurance companies are standing between patients and their treatment.  The system needs to change.   

An  applications for computers in medicine

I think about what I do as a physician and a specialist in hematology and oncology,  and how it could be made automatic. If it we're automatic, it would be done more uniformly and rigorously and the calculations would be more reliable.

I believe that machines can do many  things better than I can. Their memories are  perfect,.  Their calculations are exact.

I also believe that, at least at this point, I can add something to the machine. I can be more skeptical. I can understand the motivations of the people who create the input. I can understand the implications of suffering, the feelings of relief and loss.

I look forward to a collaboration with the machine. I do my best to foster it. I have brought the internet to clinical conferences for the last 30 years. Do not forget the sounds of the telephone modem connection in the 1980's.

I would like to see an expanded role for computers in medical decisions. .The  Electronic Medical Record was supposed to help with this integration. It has done a very poor and limited job. Sometimes, it has stood in the way of using the capabilities of computers to help analyze and solve problems. It is very difficult to export  data to a spreadsheet for analysis. This will undoubtedly get better. But the structure of incompatibility among electronic medical records is a violation of one of the main motives of the government that encouraged,  with sizable economic incentives,  their adoption.

In my hematology practice, I am often confronted with the question of the cause and treatment of low blood counts. I have found an analysis of the time course of the fall of such blood counts is useful. Often, a point in time can be identified when the fall began. A pattern can be seen suggesting either a slow, gradual fall or a rapid fall or both phenomena superimposed upon one another. Although these consultations are a welcome source of income for me, I recognize that the algorithm that I use could be made  automatic.  Such an automatic algorithm would help me do this work. Currently, an automatic solution would need to be checked by a human health care provider. Over time, the level of training of that provider might not require subspecialty certification. The primary care provider could do an adequate job of spotting unusual circumstances

I would like to see the machine identify the time course of variations in laboratory tests and then, automatically, correlate these with variations in treatment. This is not a hard algorithm. Thinking about it was somewhat entertaining. I would do it by going back to the definition of the derivative. One would then look at the slopes of the segments that make up the overall change and identify the more dramatic slopes as times of dramatic change to be correlated with alterations that might be identified in the history, especially medication changes. ( After doing the calculations, I realized that this was probably  the beginning of Newton’s method of difference,  the origin of calculus that I had recently read about in James Gleick’s biography of Isaac Newton.)  

This approach could also be used to monitor whether interventions are effective. Improvements could be correlated with interventions directed at those parameters.

Such an algorithm does not replace the human It simply helps her. I think we need it.

Model curve

"derivative" curve identifying days of greater and lesser change

Understanding and Practice:

Understanding and Practice:

How deeply do I understand the bases for my medical decisions? What constitutes practical understanding?

I practice a high stakes type of medicine: hematology and oncology.  My patients have life threatening diseases.   Patients expect a highly educated, highly informed opinion about how to proceed.  They believe  that I understand the medical research  that forms the basis of the treatment plans.  What does it mean: "to understand"?

Understand is a word that consists of two components:  under and stand, both easily  comprehended English words. I can imagine this combination  of words to mean having a relationship with the subject that  does  not crush me, I can stand under the image and look at it objectively and agree.  I can bring supporting ideas to bear on the issue, see the basis of the conclusion. 

To understand disease and treatment  has a variety of its own  meanings.  There is a  quantitative aspect to understanding, it can be broad or narrow, superficial or deep. Using these terms to describe understanding reflects its architectural nature.  Understanding is a structure.  If it claims to be  tall its foundation must be very deep, it can easily topple,  a  poorly supported claim of understanding is a very insecure structure.

Within medicine, within oncology, understanding means different things. Evaluating microscopic images requires a set of cognitive skills that is quite different from evaluating the statistics of clinical trials outcome data.  The  interpretation of large data sets that describe  outcomes is  very separate from that molecular biology that describes the mechanisms of disease and recovery. Once  a particular type of interpretation is relegated to an expert, without review by the deciding physician, it become religious scientism, faith in a vaguely understood process, believed on the basis of a report there is an unrecognized underpinning of pure faith.   It is the intelligent  integration of the various facets of information that constitutes understanding.  Understanding requires skepticism and self criticism.

The practice of medicine requires sufficient understanding to know when to use a given therapy and when  not to.  It implies a knowledge of how to administer the treatment and how to deal with its consequences.  Unfortunately, in our rapidly changing world, depth has become optional.

Often, the understanding is quite superficial.  It consists of  recognizing a  pattern that identifies a disease, reviewing sets of  guidelines or  published recommendations;  deciding  among the various alternatives; beginning   treatment; and dealing with consequences.  This pattern of behavior  is not simple and requires a high degree of education and intelligence.  But it  is not what I call understanding. 

In our era,we have come to question the  value of deeper understanding . The big data approach suggest that the  analysis of a large enough data set  will yield a better set of predictions  than a model  of disease and treatment based,  theoretical "understanding" of (an imagined)  underlying mechanism. There is undoubtedly a role for this kind of agnostic knowledge.  But, an approach that denies any level of mechanistic  understanding may fail to   identify  the heterogeneity of the data set and  obscure important information. In the large dataset we are often naming a number of diverse  entities the same diagnosis.  We now recognize some of these differences and separate the abnormalities that will reliably respond to  their special treatments, but this process of differentiating molecular diagnoses is in its infancy.  It is only begining to penetrate clinical trials.

 What we currently call a diagnosis usually does not correspond to a single molecular entity.  A  diagnosis  today is  a combination of a clinical history, physical and radilogic findings, a particular microscopic appearance with certain stains,  an aberrant  collection of  surface molecules, and/or a  set of mutations  in critical genes. Whe the diagnostic lable is attached , there is often no recognition of the methods used to arrive at the diagnosis. Sometimes one set of methods is applied, sometimes another

The important,  practical goal  is the  identification of a  distinguishing mark that directs therapy, the piece of information that will lead to cure.   Finding that is something I can support.  When I have that piece of information, in a practical sense,  I understand.