Clinical trials efficacy data

The development of nucleic acid-based therapeutics is not as straightforward as researchers had initially anticipated. Stability, toxicity, specificity, and delivery of the compounds continue to be challenging issues that need further optimization. In recent years, researchers have come up with intricate solutions that have greatly improved the efficacy of potential antisense, ribozyme, as well as RNAi-based therapeutics. Clinical trials for all these types of nucleic acid-based therapeutics are underway. So far, data from several trials and studies in animal models look promising, in particular, the therapies that trigger the RNAi pathway. However, history has shown that compounds that do well in phase I or phase II clinical trials may still fail in phase III. A striking example is the nonspecific suppression of angiogenesis by siRNA via toII-Iike receptor 3 (Kleinman et al. 2008). It will become clear in the near future which compounds will make it as a new class of antiviral therapeutics. 

Inhibitors of AR have been demonstrated to prevent a wide variety of biochemical, functional and structural alterations in animal models of diabetes. Early studies demonstrated arrest of both early cataract development and nerve conduction velocity. At least 30 clinical trials of AR inhibitors have been published involving nearly 1000 patients in total. However, there is little impressive data of their efficacy up to now but, rather than undermine the hypothesis linking excess polyol pathway activity to diabetic complications, it may reflect methodological difficulties and trial design errors. 

There are different ways to classify clinical trial data. As mentioned earlier, data can be classified by their physical nature into discrete chunks or as a more continuous measurable quantity. In clinical trials there are other important contextual ways of grouping data as well. For instance, clinical trials are primarily focused on determining two things about a drug, biologic, or device Is it efficacious, and is it safe The data that help to answer these questions are broadly classified as efficacy data and safety data, respectively. 

In this form, event would be replaced by some clinical finding such as myocardial infarction, stroke, seizure, or the like. This example form is extremely simplified, as there are usually a number of associated data variables captured as well. The event/endpoint page data must be clean, because it likely captures the primary efficacy data for the clinical trial. 

Although one cannot readily identify any comprehensive, official pronouncement on the topic of development bioequivalency, it is apparent that in many cases FDA officials have adopted a pragmatic and common-sense approach to problems of this type. Obviously, in development bioequivalency our fundamental objective should be, for example, to build an appropriate bridge between F3 and F3 and F2 and F3 such that it is legitimate to use data obtained in clinical trials with F3 and F2 to support a conclusion of safety and efficacy for F3. Depending on how substantial the differences are between Fi and F3 and F2 and F3, the required bridge can be very simple or possibly more elaborate. 

The data coming out of the trial also need to be publicly available, along with details of the methods by which the data were collected and the publications that have resulted from it. Without this, researchers reviewing the clinical-trial literature will not be able to make accurate assessments of the efficacy of medications, and doctors prescribing drugs to their patients will not have sufficient information to make informed recommendations. 

On completion of phase III trials, the data will be checked to see that it fulfils all the criteria required to generate a viable, marketable drag. The company will then file a New Drug Application (NDA), with the intention of proving the efficacy and safety of the drug in this therapeutic application. The NDA will contain all the clinical data and all relevant preclinical data for review by the FDA. Application reviews were 16.2 months on average in 1997 [75]. 

Since then, several potent and selective sarcosine-based inhibitors have been reported in the literature. Representative examples include 5 (LY2365109) [37,38], 6 ((R)-N[3-phenyl-3-(4 -(4-toluoyl)phenoxy)-propyl] sarcosine or (R)-NPTS) [39,40], 7 [41], 8 [42], 9 (JNJ-17305600) [43], and 10 [44]. Members of these series have demonstrated efficacy in several psychosis models [38,40] and JNJ-17305600 is reportedly in Phase I clinical trials for schizophrenia (data not available) [23]. 

SSR-504734 is a potent, selective, and reversible inhibitor (IC50 = 18 nM) that is competitive with glycine [47,51]. The inhibitor rapidly and reversibly blocked the uptake of [14C]glycine in mouse cortical homogenates, which was sustained for up to 7 h. Complete cessation of blockade and return to glycine basal levels occurred prior to 24 h, which is in stark contrast to NFPS (>24 h). SSR-504734 potentiated a nearly twofold increase of NMDA receptor-mediated excitatory postsynaptic currents (EPSCs) in rat hippocampal slices and produced an increase in contralateral rotations in mice when microinjected into the striatum. Microdialysis experiments indicated that the inhibitor induced a rapid and sustained increase in extracellular glycine levels in the PFC of freely moving rats [51]. The compound also demonstrated efficacy in a variety of psychosis models [51-53]. SSR-504734 was reportedly in clinical trials for schizophrenia but discontinued after Phase I (data not disclosed) [54]. 

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