Hépatotoxicité de l'amiodarone à propos de deux observations et revue de la littérature

Causality assessment of liver injury after chronic oral amiodarone intake

BACKGROUND/AIM

The number of patients receiving amiodarone will increase in future years. As clinically significant hepatotoxicity associated with oral amiodarone is infrequent and difficult to predict, a new Bayesian-developed model is proposed to help in the causality assessment of amiodarone-induced liver injury.

METHODS

Incidence of abnormal liver enzymes in patients receiving amiodarone was obtained from placebo controlled clinical trials. Published case reports of amiodarone-induced hepatotoxicity were identified through a literature search. Maximum number of expected hepatotoxicity cases in amiodarone and placebo-treated patients was calculated using Poisson distribution. The calculated odds ratio was used as a Prior Odds (PrO) to subsequent quantification, using a Bayesian-approach, of individual amiodarone-induced hepatotoxicity likelihood.

RESULTS

PrO of amiodarone-induced hepatotoxicity was 0.48. Thirty nine amiodarone-associated hepatotoxicity case reports were retrieved. Half of published case reports developed an irreversible damage. The amiodarone Bayesian model combining information about latency period and period of remission, together with analytical parameters properly defines the toxicity profile shown in published case reports. The analytical pattern defined by this model is different from the one expected if liver injury in published cases was caused by other etiologies.

CONCLUSIONS

A method based on a Bayesian-approach, which links information from clinical trials with clinical hepatotoxicity profile from published case reports can be a useful tool for amiodarone-induced liver injury causality assessment. At present, this method is limited due to scarcity and quality of available data. Further efforts are needed to improve model ability in order to identify amiodarone-induced liver injury.

Minimising the potential for metabolic activation in drug discovery

Investigations into the role of bioactivation in the pathogenesis of xenobiotic-induced toxicity have been a major area of research since the link between reactive metabolites and carcinogenesis was first reported in the 1930s. Circumstantial evidence suggests that bioactivation of relatively inert functional groups to reactive metabolites may contribute towards certain drug-induced adverse reactions. Reactive metabolites, if not detoxified, can covalently modify essential cellular targets. The identity of the susceptible biomacromolecule(s), and the physiological consequence of its covalent modification, will dictate the resulting toxicological response (e.g., covalent modification of DNA by reactive intermediates derived from procarcinogens that potentially leads to carcinogenesis). The formation of drug-protein adducts often carries a potential risk of clinical toxicities that may not be predicted from preclinical safety studies. Animal models used to reliably predict idiosyncratic drug toxicity are unavailable at present. Furthermore, considering that the frequency of occurrence of idiosyncratic adverse drug reactions (IADRs) is fairly rare (1 in 1000 to 1 in 10,000), it is impossible to detect such phenomena in early clinical trials. Thus, the occurrence of IADRs during late clinical trials or after a drug has been released can lead to an unanticipated restriction in its use and even in its withdrawal. Major themes explored in this review include a comprehensive cataloguing of bioactivation pathways of functional groups commonly utilised in drug design efforts with appropriate strategies towards detection of corresponding reactive intermediates. Several instances wherein replacement of putative structural alerts in drugs associated with IADRs with a latent functionality eliminates the underlying liability are also presented. Examples of where bioactivation phenomenon in drug candidates can be successfully abrogated via iterative chemical interventions are also discussed. Finally, appropriate strategies that aid in potentially mitigating the risk of IADRs are explored, especially in circumstances in which the structural alert is also responsible for the primary pharmacology of the drug candidate and cannot be replaced.