Individualized PK-based prophylaxis in severe haemophilia.
Dargaud YesimXavier DelavenneD P HartS MeunierP MismettiPublished in: Haemophilia : the official journal of the World Federation of Hemophilia (2018)
Over the past decades, haemophilia management has continually evolved, with prophylaxis now considered the treatment of choice. Prophylaxis primarily seeks to prevent bleeding and haemarthrosis episodes from occurring and avert the otherwise inevitable haemophilic arthropathy. Yet, numerous unanswered issues remain. These concern dose levels, dosing intervals, ways of integrating variability in bleeding phenotype, patient age, joint status, lifestyle, physical activity, treatment adherence and individual responses to FVIII or FIX concentrates. Individualized prophylaxis may thus be paramount. One crucial tool that may allow more accurate prophylaxis regimens to be implemented is the individual pharmacokinetic (PK) study. Therefore, physicians in charge of managing those living with haemophilia must be comfortable with PK profiling in order to be in a position to tailor patients' treatment, taking into account PK data, while minimizing patients' inconvenience, discomfort, as well as, possibly, treatment costs. For optimization of prophylaxis, recent development of recombinant molecules with more attractive PK properties, such as prolonged elimination half-life, increases the choice of dosing regimens, enabling decreased frequency of dosing for some, if deemed appropriate. For each patient, PK parameters can be determined, including trough levels, AUC, and time spent under a predefined threshold, with additional pharmacodynamic (PD) parameters possibly established by means of a global coagulation test like the thrombin generation test. Most importantly, target PK/PD parameters will need to consider clinical variables like patient age, body weight, joint status, treatment adherence, number of bleeding episodes, activity index or lifestyle.
Keyphrases
- physical activity
- end stage renal disease
- chronic kidney disease
- metabolic syndrome
- body weight
- cardiovascular disease
- ejection fraction
- case report
- type diabetes
- peritoneal dialysis
- weight loss
- atrial fibrillation
- combination therapy
- depressive symptoms
- skeletal muscle
- machine learning
- glycemic control
- replacement therapy
- patient reported outcomes
- drug induced
- data analysis