A d m in is tr a ti o n o f n if ed ip in e w it h '' g ra p ef ru it '' '' ju ic e' ' is tob ea v o id edA D V IC O R 1 (Ko s) (n ia ci nex te n d ed -r el ea se / lo v a st a ti nta b le ts ) N o v emb er2 0 0 3la b el in gW a rn in g sT h e ri sko f m y o p a th y a p p ea rs tob e in cr ea se d b y h ig h le v el s o fH M G -C o A re d u ct a se in h ib it o rya ct iv it y in p la sma . L o v a st a ti n ism et a b o li ze d b y th e C Y P is o fo rm3 A 4 . C er ta ind ru g s, th a t sh a reth is m et a b o li c p a th w a y ca n ra is e th e p la sma le v el s o f lo v a st a ti na n d m a y in cr ea seth e ri sko f m y o p a th y . T h es e in cl u d ecy cl o sp o ri n e, it ra co n a zo le , k et o co n a zo le a n d o th era n ti fu n g a la zo le s, th e m a cr o li d e a n ti b io ti cser y th ro m y ci n a n dcl a ri th ro m y ci n , H IVp ro te a sein h ib it o rs , th e a n ti d ep re ss a n tn ef a zo d o n e, o r la rg e q u a n ti ti eso f '' g ra p ef ru it '' '' ju ic e' ' (g re a te rth a n 1 q u a rt d a il y )P re ca u ti o n sS h o u ldn o t b e a d m in is te re d w it h '' g ra p ef ru it '' '' ju ic e' 'C li n ic a l P h a rma co lo g y :P h a rma co k in et ic s a b so rp ti o nL o v a st a ti n a b so rp ti o n a p p ea rs tob e in cr ea se d b y a t le a st 3 0 %b y'' g ra p ef ru it '' '' ju ic e' '; h o w ev er , th e ef fe ct is d ep en d en t o n th ea m o u n t o f '' g ra p ef ru it '' '' ju ic e' ' co n su m eda n d th e in te rv a lb et w ee n '' g ra p ef ru it '' '' ju ic e' ' a n d lo v a st a ti n in g es ti o nM E V A C O R 1 (Mer ck )(L o v a st a ti n ) ta b le ts Ju n e 2 0 0 2la b el in gC li n ic a l P h a rma co lo g y :A b so rp ti o nL o v a st a ti n is a su b st ra te fo r C Y P 3 A 4 (s ee'' P re ca u ti o n s: D ru gIn te ra ct io n s' ') . '' G ra p ef ru it '' '' ju ic e' ' co n ta in s o n e o r m o reco m p o n en ts th a t in h ib it C Y P 3 A 4 a n d ca n in cr ea seth e p la sma co n ce n tr a ti o n s o f d ru g s m et a b o li ze d b y C Y P 3 A 4 . Ino n e st u d y(1 ), 1 0 su b je ct s co n su m ed2 0 0 m L o f d o u b le -s tr en g th'' g ra p ef ru it '' '' ju ic e' ' (o n e ca n o f fr o ze n co n ce n tr a te d il u te d w it ho n e ra th erth a n th re e ca n s o f w a te r) th re e ti m esd a il y fo r tw od a y s a n d a n a d d it io n a l 2 0 0 m L d o u b le -s tr en g th'' g ra p ef ru it '''' ju ic e' ' to g et h erw it h a n d 3 0 a n d 9 0 m info ll o w in g a si n g le d o seo f 8 0 m g lo v a st a ti n o n th e th ir d d a y . T h is re g im eno f'' g ra p ef ru it '' '' ju ic e' ' re su lt edin m ea n in cr ea se s inth eco n ce n tr a ti o n o f lo v a st a ti n a n d it s b et a -h y d ro x y a ci d m et a b o li te(C o n ti n u ed )T able 4L a b el in g E x a m p le s o f D ru g – G ra p ef ru it Ju ic e In te ra ct io n s (C o n ti n u ed )D ru g n a m eL a b el in g se ct io nL a b el in g(a s m ea su re d b y th e a re a u n d erth e co n ce n tr a ti o n -t im e cu rv e) o f1 5 -f o lda n d fi v ef o ldre sp ec ti v el y (a s m ea su re d u si n g a ch emic a la ss a y — li q u id ch ro m a to g ra p h y / ta n d emm a ss sp ec tr o m et ry ). Ina se co n d st u d y , 1 5 su b je ct s co n su m edo n e 8 o z g la ss o f si n g le -st re n g th'' g ra p ef ru it '' '' ju ic e' ' (o n e ca n o f fr o ze n co n ce n tr a ted il u te d w it h th re e ca n s o f w a te r) w it h b re a k fa st fo r th re eco n se cu ti v e d a y s a n d a si n g le d o seo f 4 0 m g lo v a st a ti n inth eev en in g o f th e th ir d d a y . T h is re g im eno f '' g ra p ef ru it '' '' ju ic e' 're su lt edin a m ea n in cr ea sein th e p la sma co n ce n tr a ti o n (a sm ea su re d b y th e a re a u n d erth e co n ce n tr a ti o n -t im e cu rv e) o fa ct iv e a n d to ta l H M G -C o A re d u ct a se in h ib it o rya ct iv it y [u si n g av a li d a te d en zy m e in h ib it io n a ss a y d if fe re n t fr o m th a t u se d in th efi rs t st u d y , b o thb ef o re(f o r a ct iv e in h ib it o rs ) a n d a ft er(f o r to ta lin h ib it o rs ) b a seh y d ro ly si s] o f 1 .3 4 -f o lda n d 1 .3 6 -f o ld ,re sp ec ti v el y , a n d o f lo v a st a ti n a n d it s (b et a )- h y d ro x y a ci dm et a b o li te (mea su re d u si n g a ch emic a l a ss a y — li q u idch ro m a to g ra p h y / ta n d emm a ss sp ec tr o m et ry ) o f 1 .9 4 -f o lda n d1 .5 7 -f o ld , re sp ec ti v el y . T h e ef fe ct o f a m o u n ts o f '' g ra p ef ru it '''' ju ic e' ' b et w ee n th o seu se d inth es e tw o st u d ie s o n lo v a st a ti np h a rma co k in et ic s h a s n o t b ee n st u d ie dW a rn in g sT h e ri sko f m y o p a th y / rh a b d o m y o ly si sis in cr ea se d b y co n co m it a n t u seo f lo v a st a ti n w it h th e fo ll o w in g :P o te n t in h ib it o rs o f C Y P 3 A 4 : C y cl o sp o ri n e, it ra co n a zo le ,k et o co n a zo le , er y th ro m y ci n , cl a ri th ro m y ci n , H IVp ro te a sein h ib it o rs , n ef a zo d o n e, o r la rg e q u a n ti ti eso f '' g ra p ef ru it '''' ju ic e' ' (g re a te r th a n 1 q u a rt d a il y ), p a rt ic u la rl y w it h h ig h erd o se s o f lo v a st a ti n (s eeb el o w : '' C li n ic a l P h a rma co lo g y :P h a rma co k in et ic s; P re ca u ti o n s: D ru g In te ra ct io n s, C Y P 3 A 4In te ra ct io n s' ')B IL T R IC IDE 1 (B a y er )(p ra zi q u a n te l) ta b le ts A u g u st2 0 0 4 la b el in gC li n ic a l P h a rma co lo g y'' G ra p ef ru it '' '' ju ic e' ' w a s re p o rt edto p ro d u cea 1 .6 -f o ld in cr ea se inth e C m a x a n d a 1 .9 -f o ldin cr ea sein th e A U C o f p ra zi q u a n te lD ru g – d ru g in te ra ct io n sH o w ev er , th e ef fe cto f th is ex p o su rein cr ea seo n th e th er a p eu ti cef fe ct a n d sa fe tyo f p ra zi q u a n te l h a s n o t b ee n sy st ema ti ca ll yev a lu a te dC E N E S T IN 1 (Du ra m ed )(s y n th et ic co n ju g a te des tr o g en s, A ) F eb ru a ry2 0 0 4la b el in gC li n ic a l P h a rma co lo g y : D ru g –d ru g in te ra ct io n sIn h ib it o rs o f C Y P 3 A 4 su cha s er y th ro m y ci n , cl a ri th ro m y ci n ,k et o co n a zo le , it ra co n a zo le , ri to n a v ir a n d '' g ra p ef ru it '' '' ju ic e' 'm a y in cr ea sep la sma co n ce n tr a ti o n s o f es tr o g en s a n d m a y re su ltin si d e ef fe ct scC IAL IS 1 (L il ly IC O S )(t a d a la fi l) N o v emb er2 0 0 3la b el in gP re ca u ti o n s: D ru g – d ru gin te ra ct io n sK et o co n a zo le (4 0 0 m g d a il y ), a se le ct iv e a n d p o te n t in h ib it o r o fC Y P 3 A 4 , in cr ea se d ta d a la fi l 2 0 -mg si n g le -d o seex p o su re(A U C )b y 3 1 2 % . O th erC Y P in h ib it o rs — A lt h o u g h sp ec ifi c in te ra ct io n sh a v e n o t b ee n st u d ie d , o th erC Y P 3 A 4 in h ib it o rs , su cha ser y th ro m y ci n , it ra co n a zo le , a n d '' g ra p ef ru it '' '' ju ic e, '' w o u ldli k el y in cr ea seta d a la fi l ex p o su re .
Interest in drug-drug interactions has increased because of the rise in polypharmacy, where patients may take many drugs in the course of a day. Pharmacokinetic drug-drug interactions result from alteration in the dose/systemic exposure relationship, as reflected in a blood or plasma concentration–time curve, when an interacting drug induces or inhibits one or more routes of elimination or transport of a substrate drug. Assessment of a potential drug-drug interaction begins with an understanding of the absorption, distribution, and elimination processes for both substrate and interacting drugs. Metabolic drug-drug interactions involving cytochrome P450 3A4 may require special consideration because they may occur in the wall of the gastrointestinal tract and/or the liver. Improved understanding of the metabolic basis of drug-drug interactions allows the use of more informative approaches to choosing substrates and potential interacting drugs. Information gathered in properly conducted in vitro and clinical studies of drug metabolism, drug absorption, and drug-drug interactions provides critical data for drug development decisions.
The complexity and costs associated with traditional randomized, controlled trials have increased exponentially over time, and now threaten to stifle the development of new drugs and devices. Nevertheless, the growing use of electronic health records, mobile applications, and wearable devices offers significant promise for transforming clinical trials, making them more pragmatic and efficient. However, many challenges must be overcome before these innovations can be implemented routinely in randomized, controlled trial operations. In October of 2018, a diverse stakeholder group convened in Washington, DC, to examine how electronic health record, mobile, and wearable technologies could be applied to clinical trials. The group specifically examined how these technologies might streamline the execution of clinical trial components, delineated innovative trial designs facilitated by technological developments, identified barriers to implementation, and determined the optimal frameworks needed for regulatory oversight. The group concluded that the application of novel technologies to clinical trials provided enormous potential, yet these changes needed to be iterative and facilitated by continuous learning and pilot studies.
The discussion about health equity in the United States frequently involves concerns over racial and ethnic minority under-representation in clinical trials and particularly in trials conducted in support of product approvals. The FDA has long worked to encourage diverse participation in clinical trials and through its Drug Trials Snapshots (DTS) program, the U.S. Food and Drug Administration (FDA) has moved to make trial demographic data more accessible and transparent. We conducted a demographic study of U.S. participants in clinical trials for FDA-approved new drugs (new molecular entities [NMEs], and original Biologics License Applications [BLAs]) from 2015 to 2019, as reported in DTS database with a purpose of understanding the extent to which U.S.-based trials used to support product approvals represent the racial and ethnic diversity of the U.S. population by therapeutic area.Participant-level trial data were collected by accessing the FDA electronic common technical document (eCTD), for the applications used to publish each Snapshot. The therapeutic area (TA) for each drug was determined by review division assignment. The demographic data were analysed and compared to U.S. census data.We examined 102,596 U.S. participants in trials of new drugs that were approved and presented in Drug Trials Snapshots between 2015 and 2019. White participation ranged from 51% in psychiatric trials to 90% in cardiovascular (CV) trials; Black or African American participation ranged from 5% in medical imaging to 45% in psychiatric trials; Asian participation ranged from 0.75% in CV to 4% in dermatologic trials; and Hispanic or Latino participation ranged from 1% in medical imaging to 22% in infectious diseases and gastroenterology trials.Our data showed variable representation of racial and ethnic minorities across therapeutic areas at the U.S. sites. Blacks or African Americans were represented at or above U.S. census estimates across most therapeutic areas, while Asians and American Indian or Alaska Natives were consistently underrepresented. Hispanic or Latino participation across most therapeutic areas was below U.S. census estimates, however, more variable, and a sizable proportion of data was missing. The next step is a comparison of trial participation based on disease prevalence and epidemiology, which is a more accurate assessment of trial diversity.
Over the past decade, personalized medicine has received considerable attention from researchers, drug developers, and regulatory agencies. Personalized medicine includes identifying patients most likely to benefit and those most likely to experience adverse reactions in response to a drug, and tailoring therapy based on pharmacokinetics or pharmacodynamic response, as well. Perhaps most exciting is finding ways to identify likely responders through genetic, proteomic, or other tests, so that only likely responders will be treated. However, less precise methods such as identifying historical, demographic, or other indicators of increased or reduced responsiveness are also important aspects of personalized medicine. The cardiovascular field has not used many genetic or proteomic markers, but has regularly used prognostic variables to identify likely responders. The development of biomarker-based approaches to personalized medicine in cardiovascular disease has been challenging, in part, because most cardiovascular therapies treat acquired syndromes, such as acute coronary syndrome and heart failure, which develop over many decades and represent the end result of several pathophysiological mechanisms. More precise disease classification and greater understanding of individual variations in disease pathology could drive the development of targeted therapeutics. Success in designing clinical trials for personalized medicine will require the selection of patient populations with attributes that can be targeted or that predict outcome, and the use of appropriate enrichment strategies once such attributes are identified. Here, we describe examples of personalized medicine in cardiovascular disease, discuss its impact on clinical trial design, and provide insight into the future of personalized cardiovascular medicine from a regulatory perspective.
