Understanding and quantifying adherence and its link to therapeutic success

It is known that increased adherence to appropriately prescribed drugs is associated with better therapeutic outcomes and contributes to lower mortality. Adherence is described by three processes, namely initiation, implementation and discontinuation. The use of electronic monitoring e.g. Medication Event Monitoring System (MEMS) has enabled a quantitative understanding of the three processes of adherence. This includes delayed initiation, early discontinuation and particularly those around implementation including timing variability, random missed doses and drug holidays. There have been many attempts to improve adherence. An alternative approach to assist patients with suboptimal adherence to still attain therapeutic success lies in the choice of forgiving drugs. Forgiveness is a drug specific property that determines how sensitive therapeutic success is under imperfect adherence. The overarching aim of this thesis was to quantify adherence, influences of factors on adherence and the influence of adherence on therapeutic success. This involved a series of investigations. Initially, the independent influence of various factors on adherence in two diseases studied, i.e. HIV and hypertension, was determined. The factors included disease, age and dosing regimen. A model-based meta-analysis (MBMA) was adopted in this work to allow for multivariate analyses and continuous dependent variables. It was found that (1) although the influence of disease on adherence was significant, it is likely to be of limited clinical significance (2) increased age positively impacts on adherence and (3) the greater frequency of dosing regimens negatively impacts on adherence. Various measures of adherence were found to be used in the MEMS literature. Despite the advanced ability of MEMS to record patterns of drug taking, percentage of doses taken was the most commonly used measure. Appropriate summary measures of adherence in relation to adherence patterns are suggested in this thesis. These included percentage of days with correct dosing in conjunction with the number as well as the occurrence of missed doses within a timeframe. The feasibility of conducting the first MEMS study in New Zealand was undertaken. This study provided suggestions for future MEMS studies in terms of patient identification, recruitment and retention. Collected adherence data were summarised in relation to adherence patterns. A criterion to quantify the forgiveness of drugs to imperfect adherence was developed. The criterion is described as relative forgiveness (RF). RF is defined as the number of times more likely that target success is attained under perfect adherence compared to imperfect adherence. RF covers the quantification of forgiveness in two scenarios, namely (1) forgiveness of a given drug; and (2) forgiveness between two drugs whose effects can be quantified on the same biomarker of response. Subsequently, RF was illustrated with a hypothetical example and then applied to warfarin as a motivation example. This work was considered at the population level. The developed relative forgiveness criterion was applied to atorvastatin and omeprazole at the individual patient level. Hence, an individual patient’s clinically observed adherence profile, obtained from the MEMS feasibility study, was used. This study evaluated that RF is generalisable to other drugs of interest. In addition, it can be used at an individual patient level in terms of each patient’s adherence profile. Ultimately, whether or not a drug is forgiving for each patient depends on the individual adherence profile in conjunction with the individual PKPD properties. In conclusion, better understanding of factors influencing adherence was provided. Adherence data in terms of adherence patterns were described. Ultimately, the time course of drug effects in relation to adherence patterns was quantified. This allows for determining of the forgiveness of drugs under imperfect adherence patterns