Advanced liver disease is characterized by decreased hepatic drug metabolism and portacaval shunts that decrease clearance cheap 20 gm eurax with amex acne 2009 dress, particularly first-pass clearance buy cheap eurax 20 gm on-line acne juvenil. Moreover buy eurax with a visa skin care wholesale, affected patients frequently have other profound disturbances of homeostasis, such as coagulopathy, severe ascites, and altered mental status. In heart failure (see Chapter 25), hepatic congestion can lead to decreased clearance with a corresponding increased risk for toxicity with usual doses of certain drugs, including some sedatives, lidocaine, and beta blockers. On the other hand, gut congestion can lead to decreased absorption of oral drugs and decreased effects. In addition, patients with heart failure may demonstrate reduced renal perfusion and require dose adjustments on this basis. Heart failure also is characterized by a redistribution of regional blood flow, which can lead to reduced volume of distribution and enhanced risk for drug toxicity. Lidocaine probably is the best-studied example; loading doses of lidocaine should be reduced in patients with heart failure because of altered distribution, whereas maintenance doses should be reduced in both heart failure and liver disease because of altered clearance. Age also is a major factor in determining drug doses, as well as sensitivity to drug effects. Doses in children generally are administered on an mg/kg body weight basis, although firm data to guide therapy are often not available. Variable postnatal maturation of drug disposition systems may present a special problem in the neonate. Older persons often have reduced creatinine clearance, even those with a normal serum creatinine level, and dosages of renally excreted drugs should be adjusted accordingly (see Chapter 88). Diastolic dysfunction with hepatic congestion is more common in older adults, and vascular disease and dementia often occur, which can lead to increased postural hypotension and risk of falling. Therapies such as sedatives, tricyclic antidepressants, or anticoagulants should be initiated only when the practitioner is convinced that the benefits of such therapies outweigh this increased risk. Drug Interactions As a result of therapeutic successes not only in heart disease but also in other disease areas, cardiovascular physicians are increasingly encountering patients receiving multiple medications for noncardiovascular indications. Drug interactions may be based on altered absorption, distribution, metabolism, or excretion. A trivial example is the co-administration of two antihypertensive drugs, leading to excessive hypotension. Similarly, co-administration of aspirin and warfarin leads to an increased risk for bleeding, although benefits of the combination also can be demonstrated. The most important principle in approaching a patient receiving polypharmacy is to recognize the high potential for drug interactions. A complete medication history should be obtained from each patient at regular intervals; patients will often omit topical medications such as eye drops, health food supplements, and medications prescribed by other practitioners unless specifically prompted. Each of these, however, carries a risk of important systemic drug actions and interactions. As with many other interactions, this may not be a special problem provided both drugs are continued. Similarly, initiation of an inducer may lead to greatly lowered cyclosporine concentrations and a risk of organ rejection. A number of natural supplements have been associated with serious drug toxicity (e. Incorporating Pharmacogenetic Information Into Prescribing The identification of polymorphisms associated with variable drug responses naturally raises the question of how these data could or should be used to optimize drug doses, avoid drugs likely to be ineffective, and avoid drugs likely to produce major toxicities. Despite the intuitive appeal of a pharmacogenetically guided approach to drug therapy, however, practitioners wanting to adopt genetic testing to guide drug therapy encounter substantial practical barriers, including cost, varying levels of evidence supporting a role for genetics, and implementation issues such as how fast and accurately a genetic test result can be delivered. The nature of pharmacogenetic variation is that most patients will display average responses to most drugs, so systematically testing every patient in the hopes of finding the minority likely to display aberrant responses is cumbersome and seems inefficient in terms of time and cost unless the benefit for individual patients is large. An example of a large benefit is that routine genotyping of all patients receiving the antiretroviral agent abacavir is now the standard of care because 24 it avoids a potentially life-threatening skin reaction in 3% of patients. By contrast, randomized clinical 25-27 trials suggest either no effect or a modest effect on time within therapeutic range when genotype information is incorporated into warfarin dosing. A difficulty with such drug-specific approaches is that the benefit of the genotype data must be large to justify the cumbersomeness and cost of testing all exposed individuals. Although the probability is small that genetic variation plays an important role in predicting the response of an individual patient to a specific drug, when many drugs are prescribed for a population of patients, each patient will display genetically determined aberrant responses to some drugs. This reasoning underlies the concept of preemptive genotyping, in which many genetic variants relevant to many variable drug responses are 30,31 assayed in patients who have not yet been exposed to the drugs. The concept is now being tested at a few medical centers, with the goals of establishing cost and benefit, understanding how 33 health care providers react, and optimizing decision support to integrate pharmacogenomic information seamlessly into health care. These developments, along with improved nonpharmacologic approaches, have led to dramatically enhanced survival of patients with advanced heart disease. Thus, polypharmacy in an aging and chronically ill population is becoming increasingly common. In this milieu, drug effects become increasingly variable, reflecting interactions among drugs, underlying disease and disease mechanisms, and genetic backgrounds. Furthermore, despite advances in the Western world, cardiovascular disease is emerging as an increasing problem worldwide as infectious diseases, formerly predominant contributors to morbidity and mortality, are coming under control and smoking and the metabolic syndrome are increasing. Understanding how genetic background plays into disease susceptibility and responses to drug therapy, concepts largely tested in only European-ancestry populations to date, represents a major challenge in cardiovascular medicine. More generally, genomic medicine—the application of genetic variant information in health care—is still in its infancy, so reported associations require independent confirmation and assessment of clinical importance and cost-effectiveness before they can or should enter clinical practice. Developing approaches to establish the clinical impact of such rare variants on drug responses is an emerging challenge. This challenge is all the more acute because the cost of sequencing has fallen drastically since the completion of the first-draft human genome in 2000, and the under-$1000 whole-genome sequence is now a reality. This may be enabling for the preemptive pharmacogenomic strategy just outlined, as well as a broader vision of genome-guided health care, but presents major challenges in data storage and mining. The relationship between the prescriber and the patient remains the centerpiece of modern therapeutics. An increasingly sophisticated molecular and genetic view of response to drug therapy should not change this view, but rather complement it. Prescribers must always be vigilant regarding the possibility of unusual drug effects, which could provide clues about unanticipated and important mechanisms of beneficial and adverse drug effects. A phenome-wide association study of a lipoprotein-associated phospholipase A2 loss-of-function variant in 90 000 Chinese adults. Prevention of atrial fibrillation by bucindolol is dependent on the beta(1)389 Arg/Gly adrenergic receptor polymorphism. Cardio-oncology: how new targeted cancer therapies and precision medicine can inform cardiovascular discovery. Genotype-guided versus standard vitamin K antagonist dosing algorithms in patients initiating anticoagulation: a systematic review and meta- analysis.
Public release of performance data in changing the behaviour of healthcare consumers order eurax online pills acne holes, professionals or organisations cheap eurax 20gm with visa acne keloidalis nuchae surgery. Impact of public reporting of coronary artery bypass graft surgery performance data on market share purchase eurax in india skin care qvc, mortality, and patient selection. Hospital strategies for reducing risk-standardized mortality rates in acute myocardial infarction. Impact of Lean Six Sigma process improvement methodology on cardiac catheterization laboratory efficiency. Therapeutic recommendations are no longer based on nonquantitative pathophysiologic reasoning but instead are evidence based. Rigorously performed trials are required before gaining regulatory approval and clinical acceptance of new treatments (drugs, devices, biologics) and 3 biomarkers. Thus the design, conduct, analysis, interpretation, and presentation of clinical trials constitute a central feature of the professional life of the contemporary cardiovascular specialist and will 3,4 need to keep pace with the technology of the future. Case-control studies and analyses from registries are integral to epidemiologic and outcomes research but are not strictly clinical trials and are not 5,6 discussed in this chapter. They should also familiarize themselves with the processes of designing and implementing a research project, good clinical 7-10 practice, and drawing conclusions from the findings (eFig. A clinical trial may be designed to test for superiority of the investigational treatment over the control therapy but also may be designed to show therapeutic similarity between the investigational and the control treatments (noninferiority design) (Fig. The margin (M) for noninferiority is prespecified based on previous trials comparing the standard drug with placebo. Examples of hypothetical trials A to F are shown, of which some (trials B and C) satisfy the definition of noninferiority. P ) in population of patients with same clinical clinical characteristics and disease state. Generalizability to Related to enrollment criteria; the more Enrollment criteria of prior trials and medical practice Related to enrollment criteria; the more universe of all restrictive they are, the less generalizable are concurrent with those trials determine generalizability of restrictive they are, the less generalizable are patients with the the results to the entire universe of patients estimate of Pstandard − Pplacebo to contemporary practice. H ,0 Null hypothesis; H ,A alternative hypothesis; M, noninferiority margin; N/A, not available. In both superiority and noninferiority trials, the investigators propose a null hypothesis (H ) with the goal of the trial being to reject0 H in favor of the alternative hypothesis (H ). In superiority trials, α is usually two-sided, whereas it is one-sided in noninferiority trials. In a noninferiority trial, investigators specify a noninferiority criterion (M) and consider the investigational treatment to be therapeutically similar to control (standard) therapy if, with a high degree 11,12 of confidence, the true difference in treatment effects is less than M (see Fig. Specification of the noninferiority margin M involves considerable discussion between the investigators (advocating for clinical perception of minimally important difference) and regulatory authorities (advocating for assurance that the investigational treatment maintains a reasonable fraction of the efficacy of the standard 11-13 treatment based on previous trials). The investigational therapy may satisfy the definition of 14 noninferiority and may or may not also show superiority over the control therapy. Thus, superiority can be considered a special case of noninferiority in which the entire confidence interval for the difference in treatments falls in favor of the investigational treatment (see Fig. Investigators can stipulate that a trial is being designed to test both noninferiority and superiority (see Table 5. For a trial that is configured as a noninferiority trial, it is acceptable to test for superiority conditional on having 15 demonstrated noninferiority. The reverse is not true—trials configured for superiority cannot later test for noninferiority unless the margin M was prespecified. Regardless of the design of the trial, it is essential that investigators provide a statement of the hypothesis being examined, using a format that permits biostatistical assessment of the results (see eFig. By convention, α is set at 5%, indicating a willingness to accept a 5% probability that a significant difference will occur by chance when there is no true difference in efficacy. Regulatory authorities may on occasion demand a more stringent level of α—for example, when a single large trial is being proposed rather than two smaller trials—to gain approval of a new treatment. The value of β represents the probability that a specific difference in treatment efficacy might be missed, so that the investigators incorrectly fail to reject H when there is a true difference in efficacy0. The power of the trial is given by the quantity (1 − β) and is selected by the investigators, typically between 80% and 16 90%. Using the quantities α, β, and the estimated event rates in the control group, the sample size of the trial can be calculated with formulas for comparison of dichotomous outcomes or for a comparison of the rate of development of events over a follow-up period (time to failure). The allocation of subjects to control and test treatments is not determined but is based on an impartial scheme (usually a computer algorithm). Randomization reduces the likelihood of patient selection bias in allocation of treatment, enhances the likelihood that any baseline differences between groups are random so that comparable groups of subjects can be compared, and validates the use of common statistical tests. Randomization may be fixed over the course of the trial or may be adaptive, based on the distribution of treatment assignments in the trial to a given point, 15,19 baseline characteristics, or observed outcomes (Fig. Fixed randomization schemes are more common and are specified further according to the allocation ratio (equal or unequal assignment to study groups), stratification levels, and block size (i. Regulatory authorities are concerned about protection of the trial integrity and the studywise 19 α level when adaptive designs are used in registration pathway trials. The most desirable situation is for the control group to be studied concurrently and to comprise subjects distinct from those of the treatment group. Other trial formats that have been used in cardiovascular investigations include nonrandomized concurrent and historical controls (Fig. Depending on the clinical circumstances, the control agent may be a placebo or a drug or other intervention used in active treatment (standard of care). Allocation to the treatment groups occurs through a randomization scheme, subjects are followed, and the primary endpoint is ascertained. When the modification is in response to data external to the trial, it is referred to as a “reactive revision” (left side). When the investigators prospectively plan an analysis of interim data for the purposes of modifying the trial, it is referred to as an “adaptive design. Systems pharmacology, pharmacogenetics, and clinical trial design in network medicine. Withdrawal of digoxin from patients with chronic heart failure treated with angiotensin-converting-enzyme inhibitors. In this type of trial design, clinicians do not leave the allocation of treatment in each patient to chance, and patients are not required to accept the concept of randomization. It is, however, difficult for investigators to match subjects in the test and control groups for all relevant baseline characteristics, introducing the possibility of selection bias, which could influence the conclusions of the trial. Clinical trials that use historical controls compare a test intervention with data obtained earlier in a nonconcurrent, nonrandomized control group (see Fig. Potential sources for historical controls include previously published trials in cardiovascular medicine and electronic databases of clinic populations or registries. The use of historical controls allows investigators to offer the treatment(s) being investigated to all subjects enrolled in the trial. The major drawbacks are the potential for bias in the selection of the control population and failure of the historical controls to reflect accurately the contemporary picture of the disease under study. The appeal of this design is that the same subject is used for both test and control groups, thereby diminishing the influence of interindividual variability and allowing a smaller sample size.
The intermo- lecular interaction of L2 has been suggested responsible for capsid stabilization or possibly their initial formation in vivo purchase 20gm eurax visa acne you first. L1 can assemble themselves alone into the L1-capsid particles in vivo and in vitro buy 20 gm eurax amex skin care tips in hindi, by intercapsomeric disulﬁde bonds generic eurax 20 gm skin care solutions. Since L2 enhances assembly of L1 capsomeres in the absence of disulﬁde bonding, hydro- phobic interactions between L2 and L1 are most likely to initiate early assembly events (Ishii et al. In addition to its structural roles in capside, L2 protein plays multifunctional roles in genome encapsidation (Holmgren et al. As researchers continue to pry apart the intricate interactions of these comparatively few viral proteins, it is hoped that new insight at the molecular level will result in drugs to better treat this disease. The bovine papillomavirus origin of replication requires a binding site for the E2 transcriptional activator. Targeting the E1 replication protein to the papillomavirus origin of replication by complex formation with the E2 transactivator. The E1 helicase of human papillomavirus type 11 binds to the origin of replication with low sequence speciﬁcity. Binding of the human papillomavirus E1 origin-recognition protein is regulated through complex formation with the E2 enhancer-binding protein. Binding of the E1 and E2 proteins to the origin of replication of bovine papillomavirus. Remodeling of the human papillomavirus type 11 replication origin into discrete nucleoprotein particles and looped structures by the E2 protein. Proteins Encoded by the Human Papillomavirus Genome and Their Functions 35 Conger, K. Interactions between the viral E1 protein and two subunits of human dna polymer- ase alpha/primase. Crystal structures of two intermediates in the assembly of the papillomavirus replication initiation complex. Adjacent residues in the E1 initiator beta-hairpin deﬁne different roles of the beta-hairpin in Ori melting, helicase loading, and helicase activity. Interaction between cyclin-dependent kinases and human papillomavirus replication-initiation protein E1 is required for efﬁcient viral replication. Identiﬁcation of a nuclear export signal sequence for bovine papillomavirus E1 protein. Human papillomaviruses activate caspases upon epithelial differentiation to induce viral genome ampliﬁcation. Dose-dependent regulation of the early promoter of human papillomavirus type 18 by the viral E2 protein. Characterization of the human papillomavirus E2 protein: evidence of trans-activation and trans-repression in cervical keratinocytes. Differential requirements for conserved E2 binding sites in the life cycle of oncogenic human papillomavirus type 31. In vitro synthesis of oncogenic human papillomaviruses requires episomal genomes for differentiation-dependent late expression. Intranuclear localization of human papillomavirus 16 E7 during transformation and preferen- tial binding of E7 to the Rb family member p130. Association of the human papillomavirus type 16 E7 oncoprotein with the 600-kDa retinoblastoma protein- associated factor, p600. The major human papillomavirus protein in cervical cancers is a cytoplasmic phosphoprotein. Human papillomavirus type 16 E7 oncoprotein associates with the centrosomal component gamma-tubulin. Subcellular localization of the human papillomavirus 16 E7 oncoprotein in CaSki cells and its detection in cervical adenocarcinoma and adenocarcinoma in situ. The E6 and E7 genes of the human papillomavirus type 16 together are necessary and sufﬁcient for transformation of primary human keratinocytes. Identiﬁcation of human papilloma- virus type 18 transforming genes in immortalized and primary cells. Immortalization and altered differentiation of human keratinocytes in vitro by the E6 and E7 open reading frames of human papillomavirus type 18. The human papillomavirus type 16 E7 gene encodes transactivation and transformation functions similar to those of adenovirus E1A. The human papilloma virus-16 E7 oncoprotein is able to bind to the retinoblastoma gene product. Complex formation of human papillomavirus E7 proteins with the retinoblastoma tumor suppressor gene product. Biological activities and molecular targets of the human papillomavirus E7 oncoprotein. Proteins Encoded by the Human Papillomavirus Genome and Their Functions 37 Boyer, S. E7 protein of human papilloma virus-16 induces degradation of retinoblastoma protein through the ubiquitin-proteasome pathway. Human papillomavirus type 16 E7 oncoprotein associates with the cullin 2 ubiquitin ligase complex, which contributes to degradation of the retinoblastoma tumor suppressor. Adenovirus E1A, simian virus 40 tumor antigen, and human papillomavirus E7 protein share the capacity to disrupt the interaction between transcription factor E2F and the retinoblastoma gene product. Puriﬁcation and characterization of human papillomavirus type 16 E7 protein with preferential binding capacity to the underpho- sphorylated form of retinoblastoma gene product. Homologous sequences in adenovirus E1A and human papillomavirus E7 proteins mediate interaction with the same set of cellular proteins. Efﬁciency of binding the retinoblastoma protein correlates with the transforming capacity of the E7 oncoproteins of the human papillomaviruses. The E7 oncoprotein associates with Mi2 and histone deacetylase activity to promote cell growth. Human papillomavirus E7 oncoprotein dysregulates steroid receptor coactivator 1 localization and function. Interaction of viral oncoproteins with cellular target molecules: infection with high-risk vs low-risk human papillomaviruses. Coordinated changes in cell cycle machinery occur during keratinocyte terminal differentiation. The human papillomavirus type 16 E6 and E7 oncoproteins dissociate cellular telomerase activity from the maintenance of telomere length. A cellular protein mediates association of p53 with the E6 oncoprotein of human papillomavirus types 16 or 18. Characterization of human hect domain family members and their interaction with UbcH5 and UbcH7. The E6 oncoprotein encoded by human papillomavirus types 16 and 18 promotes the degradation of p53. Proteins Encoded by the Human Papillomavirus Genome and Their Functions 39 Kumar, A. Binding of high-risk human papillomavirus E6 oncoproteins to the human homologue of the Drosophila discs large tumor suppressor protein. Membrane-associated guanylate kinases regulate adhesion and plasticity at cell junctions.
By Y. Snorre. University of the South. 2019.