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2002
   Paris, France

Assessing the robustness of competing dose regimens to incomplete compliance

Mick Looby

Novartis Pharma AG, Basel, Switzerland

In drug development, non-compliance to the prescribed regimen is the Cinderella of pharmacotherapy. However, instead of being an ignored beauty, its consequences are ugly: poor characterisation of the dose response relationship, improper selection of the optimal dosage regimen and overall increased risk to patients. The clinical pharmacology of interrupted dosing ("pharmacolapsy") is a mostly unwritten book about a frequently recurring event. One of the key decisions in drug development beyond the actual dose strength is the choice of the dose interval. Nowadays it has become almost an imperative to develop drugs that can be taken once daily. One of the reasons commonly touted for this is that patients are more compliant on QD regimens. While there seems to be a relationship between noncompliance and increased frequency of dosing, there is much evidence to support the fact that the impact of QD vs BID dosing with respect noncompliance is minor (approx. 73 vs 70%)[1]. The impact of noncompliance on a particular pharmacotherapy depends on the PK/PD properties of the drug or more precisely the pharmaceutical formulation. Drugs which have long duration of action relative to their dosage interval are more robust to noncompliance. This robustness has been coined forgiveness and is specifically defined as the difference between the drugs post-dose duration of action and the prescribed dosage interval. In order to optimise a therapy the dosing regimen should reflect the forgiveness potential of a formulation so as to minimise the effects of non-compliance. Given the pressure for QD dosing, it is often essential to provide a clear rational for recommendations beyond this mode of administration. Against this background, a method for demonstrating the robustness of competing regimens is presented A na´ve model for noncompliance A na´ve model of noncompliance tries to capture typical patient compliance behaviour. Several studies have demonstrated that the distribution of overall fraction of doses taken is skewed toward a median in the range of 70-90%, while median compliance with prescribed intervals is in the range of 20-40%. However these figures were subject to large interindividual variability. Urquhart [1] has come up with the following rule of thumb to summarise average noncompliance behaviour: one in six patients:

  • Remedicates punctually
  • Takes prescribed doses, but with somewhat erratic timing
  • Skips an occasional dose, but never more than one
  • Skips three or more sequential days' doses (a 'drug holiday') 3-4 times per year
  • Has one or more drug holidays per month 
  • Takes few or no doses, but creates the illusion of good compliance

This rule provides the basis for assigning types of behaviour to portions of a population. Under the assumption that an individual's pattern of dosing should correspond to a prescribed frequency of dose taking, and assuming that any one dosing event depends only on the occurrence of the previous dosing event, given the individual's probability density function of dosing frequencies, a Markov process can be used to describe the time series of dosing events. A probability is assigned for missing (Pmiss) a dose; if a dose is missed then a probability is assigned for taking (Ptake) the subsequent doses conditional on having missed the previous dose Pmiss controls the frequency at which doses are missed; Ptake controls the duration of drug holidays. The average duration of a drug holiday is given by: 1/Ptake-1. The drug taking behaviour as described above by the rule of sixes can then be roughly characterised by the setting appropriate values for the above probabilities. The timing of dosing can also be appropriately perturbed from the nominal dosing times. This na´ve compliance model can then be linked as the input to a population PK (/PD) model for the compound in question. The effect of incomplete compliance can be assessed through simulation by counting the number of days or dosing intervals in which adequate concentrations or target effects are achieved or maintained over the treatment period. The latter can thus be used as an index for the performance of competing regimens in the presence of noncompliance. An anonymous worked example of the model will be presented and possible extensions to the basic model will be discussed.

[1]Urquhart J. Pharmacodynamics of variable patient compliance: implications for pharmaceutical value. Advanced Drug Delivery Reviews 33 (1998) 207-219.



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