|Year : 2013 | Volume
| Issue : 1 | Page : 1-3
Consideration of Ayurvedic diagnostics in design of clinical trials
P Ram Manohar
AVP Research Foundation, Coimbatore, Tamil Nadu, India
|Date of Web Publication||18-Jun-2014|
P Ram Manohar
AVP Research Foundation, Coimbatore, Tamil Nadu
Source of Support: None, Conflict of Interest: None
|How to cite this article:|
Manohar P R. Consideration of Ayurvedic diagnostics in design of clinical trials. Ancient Sci Life 2013;33:1-3
There are many levels of evidence in medicine. The issue of selecting a method of approach to prove efficacy should take into account among other things, the cost of proving efficacy. In this, we find that randomized control trials (RCTs) are the costliest to conduct, while single case reports are the cheapest. Sir Michael Rawlins, in the 2008 Harveian Oration adds a note of caution to the medical community to not insist on RCTs where other methods of proving efficacy would suffice. He gives the example of a proven drug, Ganciclovir, used for a long time for retroviral infection in HIV patients to prevent blindness. Authorities in the European Union wanted a RCT proof of efficacy. This not only resulted in a significant amount of money being spent to prove what was already known, but also resulted in the blindness of the subjects who underwent the trial and received a placebo. This goes against the spirit of medicine as propounded by the Ayurveda βcβryas: "Tadeva yuktam. bhais.ajyam. yadβrogyβya kalpate" (medicine is that which conduces to wellbeing). Although Ayurveda has a proven history of many millennia behind it, there has been a break in tradition, new diseases have emerged, people are exposed to new causative factors, and substantial knowledge has been lost with regard to treatments and medicinal plants. In the current scenario, we may need to revalidate Ayurveda in the process of adaptation to the needs of our times and in some contexts, this may necessitate subjecting certain Ayurvedic interventions to randomized clinical trials.
Design of clinical trials in Ayurveda poses many challenges. The prime challenge is the complexity of Ayurvedic treatments, which include multiple medications, therapies, diet restrictions and life style modifications. The number of parameters involved in an effective treatment makes it difficult to develop appropriate research designs for evaluating Ayurvedic treatments in clinical trial settings. For instance, clinical trials are designed to test the efficacy of a specific drug in a particular disease against a control and placebo. However, Ayurvedic treatment is not about administering specific drugs for specific diseases. It is all about tailoring treatment for each individual and modifying it appropriately based on patient response. Furst et al. have demonstrated that complex Ayurvedic treatments can be evaluated by blinded placebo controlled randomized clinical trials.  The study comparing complex Ayurveda treatments against methotrexate in rheumatoid arthritis  won the "Excellence in Integrative Medicine Research Award" from the European Society of Integrative Medicine and has been recommended as a blue print for future studies on complementary and alternative medicine (CAM).  Witt et al. have demonstrated as to how clinical trial designs can be developed to evaluate complex Ayurvedic treatments.  Of late, we are seeing that the complex nature of Ayurvedic treatments is being taken into consideration in the design of clinical trials. It is also important to realize that the complexity of the Ayurvedic approach to diagnosis should also be taken into consideration when planning clinical studies. Ayurvedic diagnosis is different from biomedical diagnosis.
Even when Ayurveda and biomedicine work on the same patient, the method of assessment and understanding of the disease varies. Ayurveda is focused more on the imbalance that underlies the disease, whereas biomedicine is focused more on the disease process and its manifestation. In other words, Ayurvedic treatments target the underlying imbalances, whereas biomedicine targets the molecular mechanisms involved in manifestation of a particular disease. For this reason, it is not possible largely to equate biomedical diagnosis with Ayurvedic diagnosis. We can also say that Ayurveda seeks to treat the energetics (not exactly in the sense it is used in physics and chemistry) of the body, whereas biomedicine seeks to treat the molecular mechanisms of disease.
When individual patients are being treated, this does not pose much of a problem because we can consider the allopathic and Ayurvedic diagnosis together. When reporting a single case study, there is no difficulty in discussing about both the diagnoses. However, when a clinical trial is conducted with a large number of study subjects, the diagnosis in Ayurveda and biomedicine across the subjects may not match and this poses difficulties in reporting efficacy of the treatments.
To illustrate, patients with cervical spondylosis presenting almost the same findings of disc bulge and degeneration would be subjected to standard allopathic treatment. However, the Ayurvedic assessment may give different findings in different patients. A person who developed the problem by riding motorbikes in the hot sun on bumpy roads, or another person who sits for long before a computer in an air conditioned room or yet another person who has strained his neck lifting weights will be prescribed different kinds of oils by an Ayurvedic practitioner because the imbalances caused by etiological factors are different. In a clinical trial, such a situation can lead to difficulties in assessing the efficacy because treatments are individualized. The Ayurvedic physician treats what appears to be the same disease from the point of view of biomedicine with different medicines.
There are essentially two components in Ayurvedic diagnosis, which make it complicated when compared to biomedical diagnosis. These are: The assessment of the patient's constitution (prakr.ti), assessment of the disease (vikr.ti). The assessment of the constitution leads to individualization of the treatment. The assessment of the disease is dynamic. The disease is characterized in terms of the imbalance that underlies the disease as well as the evolutionary phases through which it progresses and the phases of regression induced by the administered treatment. The assessment of imbalance can also influence individualization of treatment. It is however, the dynamic assessment of the progression and regression of the disease that leads to modification of the treatment periodically. Thus, it can be said that the dual diagnosis in Ayurveda leads to individualization and modification of the treatments. This can pose severe challenges when conducting clinical trials because not only will the treatments administered to a group of study subjects be different, but the treatment will also have to be modified in the course of the trial. The RCT studies are designed to evaluate the efficacy of a single drug or combination that cannot be changed in the course of the trial under normal circumstances. In view of the above, it is clear that Ayurveda exhibits complexities that cannot be handled by current research designs used in biomedical research. This is where the concept of Whole System Research assumes significance.
There is a need to develop research designs to evaluate Ayurvedic treatments from the whole medical systems perspective.
The Medical Research Council in the United Kingdom has published a document "developing and evaluating complex interventions," which provides valuable guidance for research on complex interventions. 
Kaplan et al. have pointed out the limitations of the RCT design in evaluating complex treatments of CAM.  Fønnebø et al. have highlighted the gap between published studies showing little or no efficacy and reports of substantial benefit from real life clinical practice.  This is because CAM treatments are not researched the way they are practiced at the point of care. Walach et al. propose a circular model instead of a hierarchical model for evaluation of complex medical systems. They point out that the hierarchical model is valid only for limited questions of efficacy, especially for regulatory purposes and pharmacological products.  It was pointed out earlier that Furst et al. have demonstrated that RCT designs can be tweaked to effectively evaluate complex Ayurvedic interventions.
When we think of whole medical system we have to think of how to fit research designs to complex medical systems and not the other way round. In order to develop research designs that suit the complexity of traditional medical systems, we need to understand their innate complexity in a comprehensive manner.
The Ayurvedic treatment protocol is not primarily drug based. It is based on a complex algorithm. The treatment algorithm is derived through a complex diagnostic procedure. To understand the treatment protocol one has to get a grasp of the diagnostics in Ayurveda. In fact, when a clinical study is planned, we need to use Ayurvedic diagnostics to derive the treatment algorithm. Biomedical diagnosis, on the other hand, helps to objectively assess the treatment outcomes.
We can visualize three models here:
- Studies based exclusively on biomedical diagnosis and an attempt to fit Ayurvedic treatment into the biomedical diagnosis. This leads to trimming and modifying Ayurvedic treatment to fit the research design
- Studies based exclusively on Ayurvedic diagnosis, and therefore the treatment is based on Ayurvedic diagnosis. This approach enables to preserve the complexity of Ayurvedic treatment, but treatment outcomes cannot be objectively assessed due to lack of validated instruments to evaluate the Ayurvedic parameters
- These studies can be those where both Ayurvedic and biomedical diagnoses are included, but reporting of outcome is based on biomedical diagnosis only. In such a situation, the benefits of therapy that is not measured within the purview of biomedical methods of assessment will not be assessed. For the above reasons, it is important to include both biomedical and Ayurvedic diagnosis and assessment of outcomes in the study design. The complexity of the Ayurvedic diagnosis derives from the fact that it attempts to understand the disease in the backdrop of constitution. Furthermore, it focuses on the underlying imbalances in terms of the dos.as apart from the manifestation of the disease. Thirdly, Ayurvedic interventions are very interactive and modified according the evolution of the disease. The disease may evolve forward in progressive phases or devolve in the direction of resolution through regressive phases. This is why Ayurvedic treatment protocols are expressed in terms of algorithms and not prescriptive guidelines. The algorithms help the physician to work in a reflexive (circular cause and effect relationship), iterative (outcome of each process or iteration becomes the input for the next) and recursive manner (one step in procedure invokes the whole procedure).
Collins et al.  have proposed the multiphase optimization strategy for evaluating complex interventions using the RCT design. In this, multiple components of a treatment package are first screened, identified for inclusion, then the selected components are fine-tuned and finally the optimized intervention is subjected to randomized clinical trial. They also propose the sequential multiple assignment randomized trial, which can be used to identify the best tailoring variables and decision rules for an adaptive intervention empirically.
To sum up, Ayurvedic treatments are individualized and modified in the course of treatment based on actual responses. And these treatment decisions are continuously monitored and modified by a dynamic diagnostic procedure. Clinical research must take into consideration the complexity of the Ayurvedic approach to diagnosis to effectively understand the efficacy of these interventions. It is very important to ensure that treatments administered in a clinical trial are based on the Ayurvedic diagnostic assessment. While there is no harm in collating these assessments with biomedical diagnosis and also evaluating outcomes of treatment based on biomedical diagnosis, it would be misleading to truncate and force fit Ayurvedic treatments to suit biomedical diagnosis and research designs. Novel clinical research designs will have to be developed for testing the efficacy of Ayurvedic clinical interventions that accommodate its whole system approach. Alternatively, existing research methods like the RCT design can be suitably modified to incorporate the complexity of Ayurvedic treatments. It can be concluded that Ayurvedic diagnostics and treatment are two sides of the same coin and any attempt to study the efficacy of Ayurvedic treatments without due consideration of the diagnostic principles will be misleading.
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