Personalised Medicine
It is a much more demanding application, howev- er, to use the animal model to make decisions about the predicted therapeutic efficacy of an investigational drug. Moreover, in order to confi- dently employ even a simple, dichotomous (posi- tive vs negative) response, scientists must have a reasonable idea of how that preclinical change predicts the clinical response. For many reasons, that is very hard to do.
Phenotypically similar but etiologically heterogeneous diseases
The last consideration of causality for the low Phase II success rates is that the biological target is relevant to the disease only in a subset of patients, or is important but only in combination with other disease-driving mechanisms. Currently, the classifi- cation of many diseases and the resulting selection of therapy are based on the clinical presentation. For example, the diagnosis of diabetes mellitus is based on an elevated blood glucose or glycosylated hemoglobin value. For type 2 diabetes mellitus, however, it is the integrated impact of several fac- tors that yield the patient’s blood glucose value. Consider two patients, Mrs S and Mrs T, both of whom are 65 years of age and have fasting plasma glucose concentrations of 180mg/dL (10mM). Despite the same diagnosis of diabetes mellitus, the manner in which their respective blood glucose concentrations came to be 180mg/dL can be very different. At the next level of separation or stratifi- cation, Mrs S and Mrs T may differ in how much insulin their beta cells can produce counterbal- anced by target tissue sensitivity to insulin. Peeling the layers of the ‘pathophysiologic onion’ further is a trove of literature-generated possibilities sup- porting differential contributions in our hypotheti- cal patients from inflammation, fuel availability, genetic abnormalities, counter-regulatory hor- mones, autonomic nervous system activity, etc. Thus, these two patients may differ on multiple variables that, when summated, produce a glucose of 180mg/dL.
Diabetes mellitus is chosen as an example because, at face value, it is a relatively simple dis- ease, first diagnosed in antiquity by some simple biomarkers, ie the taste and volume of urine. Despite a simple and obvious clinical presentation, however, there are multiple factors that differen- tially contribute to any individual patient’s presen- tation (Figure 1). The same thought process is applicable to rheumatoid arthritis, Alzheimer’s disease, atherosclerosis, cancer, ie that there are often multiple ways to attain the same ‘apparent’ clinical state.
Drug Discovery World Summer 2011
Incongruence between razor-like selectivity of an investigational drug and clinical classification of disease In contrast to the more classical manner in which diseases are characterised, the discovery and development of new therapeutics is focused on the creation of agents with a high degree of molecular specificity. New drug candidates, whether NCEs or monoclonal antibodies, are designed to possess a high degree of biological selectivity. There are two main reasons for this. First, it is highly desir- able to specifically attack the target of interest and prevent ‘unwanted’ specificity spillover on to other mechanisms that are similar to the intended biological target19. This spillover has been hypothesised to play a role in unwanted side- effects of the new agent.
Second, this approach also reflects the way that we are trained to conduct experiments, ie to focus on, or manipulate, one variable at a time. Modern experimental methods, particularly since Ronald Fischer, have imprinted on generations of scientists that a well-conducted experiment should be able to conclude that a change in the dependent variable was a direct consequence of the manipulation of an independent variable. The conduct of trials in this manner will assure that the outcomes are likely and specifically due to the intended manipulation. If the tested molecule affects other biological targets beyond the primary, then how would we know that the intended mechanism is key to the new therapy’s efficacy?
The ability to selectively manipulate individual targets within large families of related targets with razor-like precision explains in part the attractive- ness of monoclonal antibodies, or newer modalities such as antisense oligonucleotides or siRNA. Although the approach to manipulating one molec- ular target at a time is very appealing (in more than one way), it stands in contrast to how most diseases are characterised, ie there is a mismatch between how we currently define disease and the approach to developing therapies and our standard discovery process lacks the basic tools to address multi-vari- ate disease etiology. That is, to more effectively treat disease, we need to directly treat its causes within the relevant patient subpopulation. Consider the following hypothetical Phase II study of a new investigational agent to lower glu- cose, illustrated in Figure 2. Based on published lit- erature, there is good rationale that a specific bio- logic target, ABCD, may be important in worsen- ing insulin resistance. Assume that the recruited study population consists of typical patients with type 2 diabetes, classed by their glucose values. The
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