Side effect overload on drug labels has less to do with true toxicity and drug safety than with manufacturer liability. Examination of more than 5600 drug labels yielded over half a million side effects. An average drug label and the more commonly prescribed drugs averaged 70 and 100 side effects, respectively. The upper range in a single label was 525 reactions. Information overload can overwhelm physicians, who must weigh the risks and benefits when prescribing a medication. The Food and Drug Administration discourages such ‘over warning,’ but information overload is presently the rule rather than the exception. Not surprisingly, medications typically used by psychiatrists and neurologists had the most complex labels, while drugs used by dermatologists and ophthalmologists had the least. Although providing drug safety information more efficiently to both health care providers and the public is warranted, drug manufacturer liability concerns must also be addressed.
Biomarker use in translational medicine is predicated upon preclinical qualification and validation – 2 distinct steps in the biomarker development process. Prior to issue in 2009 (EMA) and 2010 (FDA, PMDA) of the renal-specific DRAFT qualification guidelines, there was no clear direction by the U.S. Food and Drug Administration (FDA) or European Medicines Agency (EMA) of how companies should qualify new biomarkers for disease progression or clinical trial endpoints. The trend in biomarker use is multivariant analysis, the tracking of subtle changes in multiple biomarkers simultaneously, often utilizing various tissue types. While the new guidance addresses biomarker qualification, analytical validation of new biomarkers remains undefined. This review updates the reader of the status of both qualification and validation of translational biomarkers.
Any new drug that penetrates the central nervous system must receive some preclinical analysis of abuse liability potential (Draft Guidance). Usually, it is determined through prior knowledge of chemistry and/or pharmacology of the candidate’s drug class that compounds have abuse liability. For example, if a test article stimulates release of dopamine in the nucleus accumbens of the brain, most likely it will be abused by humans (Koob and Volkow, 2010). Discussion of nonclinical abuse liability testing requirements with regulators prior to submission of any formal materials, however, is always advised.
Behavioral pharmacology is only one facet for determining abuse liability; however, preclinical drug discrimination and self-administration data speak loudly. As outlined in O’Connor et. al. (2011), several factors can influence nonclinical drug self-administration data. Animal strain, training regimen, food restriction, duration of access, rate of infusion, and training doses can all influence self-administration data (Baladi et. al., 2010; Banks and Negus, 2010; Caroll, 1985; Kosten et. al., 1997; Lynch et. al., 2010; Woolverton, 1992). Misleading self-administration data can lead to program-killing false-positives or underestimated abuse liability that will manifest during clinical trials. Something as “unimportant” as the dose of the training compound can impact drug discrimination. Too high or too low of a training dose may alter the interoceptive cue of test article and shift dose response curves accordingly when the test article is screened (e.g., Mumford and Holtzman, 1991). These results would drastically affect interpretation of safety margin. Unchecked variables can significantly impact analysis and delay submissions. Although regulators are savvy to these variables, to the classically trained chemist, for example, these variables can seem like smoke and mirrors without the proper experience.
Daily monitoring of behavioral data and animals (weights, response patterns, and general health) is necessary to determine whether preclinical studies are being carried out properly and are subsequently valid. One must be aware that self-administration and drug discrimination studies usually take several months to complete, with animals generating data daily. Failure to incorporate appropriate controls such as presenting “inactive” levers and recording inactive lever responses can render a study invalid; this practice serves as an index of accuracy (O’Connor et. al., 2011). Additionally, catheter patency in rats used for self-administration studies is not a trivial concern. An impaired catheter can seriously alter response patterns. The same animal may alter behavior over time due to time-dependent physiological changes (e.g., behavioral tolerance) or a faulty catheter. Behavioral criteria must be established well in advance in order to accurately track animal response patterns. Frequent catheter patency tests should regularly occur.
Several nonclinical laboratories (especially academic) combat less than aseptic conditions with daily administration of antibiotics to their experimental animals to maintain catheter patency and animal health for lengthy self-administration experiments. Body weights must be maintained at certain levels to ensure motivated animals. If an animal is food restricted for eight months and administered daily antibiotics, will this create problems with your compound? Concomitant effects can potentially lead to additional toxicology studies if you have unexpected clinical signs or abnormal clinical pathology findings.
Some contract research organizations (CRO) may suggest using their “trained” animals, usually non-human primates, for preclinical drug discrimination and self-administration studies. Will the drug history of these animals pose a problem? Should you instead consider use of rats over non-human primates? At this point in time, if the metabolism and kinetics of your compound are similar in rats and humans, use of non-human primates is not necessary and may not be justified from an animal welfare standpoint (O’Connor et. al., 2011). Moreover, the behavioral database for rats is just as strong as for non-human primates (O’Connor et. al., 2011). The benefits of using non-human primates, however, are multiple. A CRO can maintain a small colony of non-human primates that are trained to self-administer or discriminate drugs of abuse for years. For this reason, animals are essentially ready for screening at initiation of the study. One should consider, however, that non-naive animals may have impacted health due to long histories of handling, laboratory conditions, implanted devices (in self-administration animals), and a history of drugs that may impact physiology and/or behavior. Will this confluence of factors negatively interact with your compound?
In conclusion, behavioral pharmacology studies should not be taken lightly, and possession of the necessary expertise and skills to navigate these challenges is necessary. Lack of experience in what was once considered a “soft science” can be extremely detrimental in drug development, costing additional time and money. Just like any scientific assessment, there are “correct” and “incorrect” ways of conducting behavioral pharmacology experiments. For this reason, many large pharmaceutical companies and CROs now have expert working groups for abuse liability screening.
References Cited
Baladi MG, Newman AH, France CP. Dopamine D3 receptors mediate the discriminative stimulus effects of quinpirole in free-feeding rats. J Pharmacol Exp Ther. 2010 Jan; 332(1):308-15.
Draft Guidance for Industry Assessment of Abuse Potential of Drugs (January, 2010) prepared by the Controlled Substance Staff (CSS) in the Center for Drug Evaluation and Research (CDER) at the Food and Drug Administration.
Kosten TA, Miserendino MJ, Haile CN, et. al. Acquisition and maintenance of intravenous cocaine self-administration in Lewis and Fischer inbred rat strains. Brain Res. 1997; 778(2):418-29.
Lynch WJ, Nicholson KL, Dance ME, et. al. Animal models of substance abuse and addiction: implications for science, animal welfare, and society. Comp Med. 2010; 60(3):177-88.
Mumford GK, Holtzman SG. Qualitative differences in the discriminative stimulus effects of low and high doses of caffeine in the rat. J Pharmacol Exp Ther. 1991 Sep; 258(3):857-65.
O’Connor EC, Chapman K, Butler P, Mead AN. The predictive validity of the rat self-administration model for abuse liability. Neurosci Biobehav Rev. 2011; 35(3):912-38.
Paul Kruzich is an experienced abuse liability and safety pharmacology consultant. He has extensive industrial/CRO experience as a study director and academic experience as a tenure-track faculty member at the Medical College of Georgia. His professional affiliations include the College on Problems of Drug Dependence, Safety Pharmacology Society, Society of Toxicology, and Society for Neuroscience. Dr. Kruzich has authored over 23 peer-reviewed articles and 2 book chapters and has served as a reviewer for over 5 scientific journals.
By utilizing the basic principles of hemodynamics and hydraulics, research suggests that fluid retention is detrimental for the cardiovascular system because it increases the likelihood of turbulent blood flow, regardless of whether or not blood pressure is raised. Increased turbulence promotes endothelial dysfunction, thereby contributing to the development of atherosclerotic cardiovascular disease. Fluid retention induces hypertension in some individuals, increases stroke volume (the amount of blood that is ejected by the heart with each contraction) in others, and causes edema. Some blood pressure lowering medications also increase stroke volume and cause edema but prevent heart attacks and strokes when used to treat hypertension. For drugs that increase the risk of adverse cardiovascular events, it may be possible to reduce or neutralize the increased risk by simultaneous diuretic administration.
In order to keep our competitive edge, the Federal Drug Administration (FDA) is placing increased emphasis on strengthening both the field and application of regulatory science relative to pharmaceutical research, development, review, and post-market surveillance. The FDA also has a mandate to recognize areas of unmet public health need and try to galvanize action to move appropriate new products through the pipeline and into the market. The FDA has the responsibility, therefore, not just to review and approve products if the data support that decision, but also to follow these products once marketed to answer critical questions about efficacy and safety. Examination of products across their life cycle enables not only the identification and analysis of emerging safety signals, but also facilitates the continual balancing of risks and benefits.
Research studies, both preclinical and clinical, that form the basis for approval of medical products are increasingly being performed in other countries and often in networks of other countries. For this reason, international recognition of both the scientific appropriateness and ethical conduct of those studies becomes increasingly important to global regulatory bodies. A key understanding is that if a safety concern develops for an approved drug, it does not necessarily reflect that a mistake was made. It may instead reflect new emerging knowledge about that drug in practical use. Regulatory safety has to be a dynamic process. The desire is to proactively ensure that the right studies are done so that the best possible decisions result. However, there isn’t always an absolute, clear decision to be made; resolution, therefore, requires a dynamic balancing of risks and benefits. Questions need to be asked about whether certain subpopulations of patients may benefit from targeted use of a drug, or whether the safety concerns are sufficient to mean a more active withdrawal of a product from the market. Advances in science and technology need to be better incorporated into the regulatory process, with a key area being safety science. To continue to strengthen the science of regulatory safety, the need is to broaden not only the kinds of preclinical and clinical studies that can be done to deepen our understanding of safety, but also to broaden our understanding of how to apply and weight that data to further the science of risk management.
Source: Interview between Dr. Eli Adashi, Professor of Medical Science at Brown University and host of Medscape One-on-One, and Dr. Margaret Hamburg, Commissioner of the US Food and Drug Administration. MedScape Today.
Both advantages and challenges exist for use of dried blood spots during preclinical drug development. Advantages include small sample volumes coupled with easy shipment and storage. The amount of blood per spot varies (10 to 100 μL), but use of 15 to 20 μL seems to be most common. With larger blood spots, although multiple analyses are possible from each spot, the spots are less homogeneous. For this reason, it is suggested to have 3-4 smaller spots (of 20 μL or less) which are more homogeneous, thus increasing inherent sample quality.
The small sample volumes required for dried blood spot analysis mean that fewer animals – and therefore less drug – are needed during preclinical studies relative to conventional blood analysis (milliliters of blood often required). Blood samples spotted and dried on cards don’t need to be frozen, thereby simplifying the procedures for both sampling and shipping, with subsequent cost savings. Provided a compound is stable in blood, which must be demonstrated for each compound, dried blood spot samples can be shipped in an envelope at room temperature.
In addition to the ethical and financial benefits, use of dried blood spot analysis can also improve preclinical data quality. Typically, use of multiple small animals is necessary to generate drug concentration-time curves in typical pharmacokinetic and toxicology studies, due to insufficient blood volume per animal, thus introducing a potential source of undesirable variation in the data. That source of variability can be eliminated with dried blood spot analysis. The smaller volumes associated with the technique mean that serial sampling can be performed with each animal, thereby enhancing preclinical data quality. In addition, some researchers have found that the relatively high stability of compounds in dried blood spots, especially prodrugs and their metabolites, is a key advantage of the technology.
Dried blood sample analysis has some drawbacks in that analysis is more time-consuming than that required for liquid samples, but still includes liquid chromatography and tandem mass spectrometry. The limit of resolution is not yet adequate for low-exposure drugs (e.g., pg/mL), and components of the cards on which spots are collected can interfere with some analyses. Some researchers have determined that the additional time necessary for analysis is a detriment to the speed required in discovery-phase research. In some organizations, the decision to use dried blood spots is currently being made on a program-by-program basis as drug candidates move from discovery into early-stage development. One holdup has been the impracticality of switching late-stage compounds with a long history of analyses in plasma over to dried blood spot analysis. The pharmacokinetic values obtained from liquid plasma and from dried blood are not directly comparable, and “bridging” studies are required to switch between matrices. “Even though you can generate an in vitro number for converting between blood and plasma, it doesn’t always work,” Neil Spooner, director of bioanalytical science and development at GlaxoSmithKline in Ware, England said.
Perhaps the most pressing detriment to use of dried blood spots is the need for improved automation, although some automation is available. Fully automated techniques are generally available for fluid samples, thus enabling high throughput analysis of thousands of samples. Direct analysis methods for dried blood spots, which bypass the need to create a paper punch, are under development.
To date, it is undetermined how global regulatory bodies will respond to data obtained from dried blood spot analysis. Some feel that the European Union may be more accepting than the Federal Drug Administration (FDA). The FDA declined to comment citing “insufficient experience with the technology.” Although international guidelines state that kinetics can be measured in blood, plasma, or serum, specific US guidelines for use of dried blood spot analyses are absent. Richard M. LeLacheur, vice president at PharmaNet USA, a contract research organization in Princeton, N.J., says “As the comfort level, regulatory experience, and infrastructure grow, people will realize it’s not a big leap to go into dried blood spots, and the benefits are worth it.”
For pharmaceutical companies, is personalized medicine more of a threat than an opportunity? In addition to the development of new drugs, genetic information can also help target the use of current medications (e.g., Plavix). The use of genetic (or other) information to target patient population subsets is expected to increase drug safety and render cost savings to both insurer and patient, but can it also be expected to limit the potential market and lower pharmaceutical sales? By potentially enhancing drug safety, personalized medicine is expected to elicit fewer adverse drug reactions, thereby leading to fewer liability claims against drug companies. Drug development costs rise, however, if preclinical scientists also must isolate a genetic trigger and develop a companion test for a treatment, even if the size of clinical trials can potentially be reduced and additional income can be expected through purchase of both medication and companion diagnostic. Even when a drug is utilized in target populations, how much risk will be deemed acceptable? Whether personalized medicine stimulates or inhibits pharmaceutical drug development remains to be determined.
All medical products pose risks and postmarketing surveillance is critical to expanding the limited evidence base that exists when new drug products are approved. Through initiation of the Sentinel Initiative (May 2008), the Food and Drug Administration (FDA) is developing the capacity for actively monitoring the safety/toxicity of approved medical products using the electronic health information in claims systems, inpatient and outpatient medical records, and patient registries. The pilot program, Mini-Sentinel, uses a distributed data network (rather than a centralized database) of health plans and other organizations to create data files in a standard format while maintaining physical and operational control over their own patient-level data, thus ensuring patient privacy. Laying the groundwork for that system has required input from both public and private organizations. These data partners can obtain full-text medical records, when necessary, to confirm diagnoses or exposures and to determine the existence or severity of risk factors.
The initial focus of Mini-Sentinel has been on developing the ability to use medical claims data. Over the next year, laboratory-test results and vital signs will be added. The FDA will soon begin to actively monitor the data, seeking answers to specific questions (e.g., frequency of myocardial infarction among users of oral hypoglycemic agents). Using the Mini-Sentinel system, the FDA will also be able to obtain rapid responses to new questions about medical products and, eventually, to evaluate the health effects of its regulatory actions.
Ideally, every new drug would represent an unprecedented breakthrough and lead to the creation of a
completely novel treatment. This, however, is not the reality of the pharmaceutical industry, or of any other
development-based industry. Creating drugs based on incremental innovations provides pharmaceutical
companies with a secure stream of revenue, which can be directed to higher-risk, potential blockbuster-yielding
research. Policies aimed at reducing the industry’s ability to obtain revenues from incremental innovations
could be self-defeating, as those industries will then have less revenue to reinvest in R&D for new drugs. Put
simply, limiting incremental drug innovation is analogous to limiting competition. The ultimate result could
have devastating consequences for the future of the pharmaceutical industry and for the millions of patients
who depend on it.
Ideally, every new drug would represent an unprecedented breakthrough and lead to the creation of a completely novel treatment. This, however, is not the reality of the pharmaceutical industry, or of any other development-based industry. Most new drugs represent the combined weight of seemingly small improvements achieved over time. Creating drugs based on incremental innovations provides pharmaceutical companies with a secure stream of revenue, which can then be directed to higher-risk, more innovative research. Many critics contend that “Me-too” drugs — drugs within the same chemical class as one or more already on the market — add little or no therapeutic value to existing formularies. Conversely, advocates claim that new drugs based on incremental improvements generally represent advances in safety, efficacy, selectivity, and ultimately increase the utility of drugs within a specific therapeutic class. Innovations may also include new formulations and dosing options. Changes in one or more of these parameters generally increase patient compliance and improve health outcomes. Furthermore, patients can respond differentially to drugs within a single class, thus having multiple drug options within a therapeutic class enables optimization of medical treatment to best fit a patient’s needs. From an economic standpoint, while it is unrealistic to presume that every incremental innovation leads to cost savings, the sum of all drug innovations can reduce overall treatment costs, shorten or eliminate hospitalization, increase worker productivity and reduce absenteeism, and eventually lower drug costs through increased competition among manufacturers.
In conclusion, policies aimed at reducing an industry’s ability to obtain revenue from incremental innovations could be self-defeating, as less revenue will be available to reinvest in research and development. In pharmaceutical terms, limiting incremental drug innovation is analogous to limiting competition. The result could have devastating consequences for the future of the pharmaceutical industry and ultimately for patients.
FDA approval does not mean that a drug works well; it means only that the Agency deemed its benefits to outweigh its harms. Comparative efficacy data, other than to placebo, may be missing from the label. In 2006, the FDA revised the drug label design, adding a “highlights” section to emphasize the drug’s indications and warnings. It also issued guidance about reporting trial results in the label and emphasized the importance of effectiveness data. Yet some recent label updates (e.g., for Lunesta and Rozerem) are substantively unchanged. Use of “Prescription Drug Facts Boxes,” featuring a data table of benefits and toxicities has been proposed. Recently, the FDA’s Risk Advisory Committee recommended that the FDA adopt these boxes as the standard for their communications. FDA leadership is deciding whether and how to use the boxes in reviews, labels, or both. Also proposed is the generation of a standardized executive summary of FDA drug reviews. These summaries should include data tables of the main results of the phase 3 trials, highlight reviewers’ uncertainties, and note whether drug approval was conditional upon a post-approval study. While publication of new comparative-effectiveness results is helpful, publications generally occur post approval. In contrast, much is known about drug effectiveness and drug safety at approval that could better guide physician and patient choice if this information was more widely disseminated.
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