Clinical trials steps

ICHE8 General considerations for clinical trials Step 5 (1997)... 

Probucol. Probucol is an antioxidant that is effective in lowering LDL cholesterol. Whereas probucol was known to lower cholesterol after relatively simple clinical trials (160), its mechanism of action as an antioxidant in the treatment of atherosclerosis is quite novel. Probucol has been shown to have the abiUty to produce regression of atherosclerotic lesions in animal models (161). Probucol therefore represents a novel class of pharmaceutical agent for the treatment of atherosclerosis. This effect occurs mechanistically, in part, by preventing oxidation of LDL, a necessary step in foam cell formation. This antioxidant activity has been shown in laboratory experiments and its activity in lowering LDL cholesterol in human studies is well documented (162). 

Hypokalemia. Hypokalemia associated with thia2ide diuretic therapy has been knpHcated in the increased incidence of cardiac arrhythmias and sudden death (82). Several large clinical trials have been conducted in which the effects of antihypertensive dmg therapy on the incidence of cardiovascular complications were studied. The antihypertensive regimen included diuretic therapy as the first dmg in a stepped care (SC) approach to lowering the blood pressure of hypertensive patients. 

The purification of value-added pharmaceuticals in the past required multiple chromatographic steps for batch purification processes. The design and optimization of these processes were often cumbersome and the operations were fundamentally complex. Individual batch processes requires optimization between chromatographic efficiency and enantioselectivity, which results in major economic ramifications. An additional problem was the extremely short time for development of the purification process. Commercial constraints demand that the time interval between non-optimized laboratory bench purification and the first process-scale production for clinical trials are kept to a minimum. Therefore, rapid process design and optimization methods based on computer aided simulation of an SMB process will assist at this stage. 

There are several areas of study that will help you understand the research topic. As a first step you will want to read the clinical protocol. The protocol describes the device or medication to be used, the patient populations under study, the statistical plan of the clinical trial, and the details of the disease state. If you want to understand the disease state or indication further, you may want to seek out a clinical investigator of the clinical trial or do some further reading about the disease. Understanding the patient population is a good way to understand the data that you will see and whether there is reason for concern when viewing the data. For example, if you were studying a medication to reduce hypertension, you would not be as worried if patient blood pressure data were elevated at baseline. You would expect to see this because you understand that hypertensive patients have high blood pressure. 

Clinical trial data come in two basic forms numeric variables and text variables. Numeric variables are easy for the statistical programmer to handle. Numbers can be analyzed with SAS in a continuous or categorical fashion without much effort. If a numeric variable needs categorization, it is easy enough to categorize the data within SAS. For example, if you had to classify patient age, a simple DATA step such as the following might serve well. 

There may be times when a DATA step with arrays is a better means to transpose data. This is true when the data to be transposed have more than one record per BY group variable or when there is a need to have the resulting data set include data that are not in the source data set. In clinical trials missing data is a very common issue. Let s look at a derivation of the previous systolic blood pressure transposition problem where visit 2 is always missing. 

Notice the missing column for visit 2. This is exactly what you would expect PROC TRANSPOSE to give you. PROC TRANSPOSE transposed the data that were present and could not be expected to know about visits that are not represented in the data. However, often in clinical trials reporting you want to report on all visits, treatments, or other expected parameters whether they are represented in the actual data or not. In this case, a DATA step with arrays is a better choice to transform the data. Here is an example of the previous transposition that includes all visits 1-5, regardless of which visits are included in the underlying data. 

Note that for generating tables there are single SAS procedures such as PROC TABULATE and PROC REPORT that can perform all of the preceding four steps. We will examine PROC TABULATE and PROC REPORT as possible reporting methods after you see what a typical clinical trial table looks like. 

The previous sections show you how to extract / -values for a commonly used set of statistical tests. This section describes a general step-by-step approach for getting your statistics from a SAS procedure into data sets for clinical trial table or graph reporting. Here are the steps to follow ... 

Another class of DNA alkylating agents, the Mitomycins, proved to be most promising in clinical trials. Among these, mitomycin C, shown in Fig. 6.1, exhibits significant anti-tumor activity. Its mechanism of activation consists of a complex bioreductive process. The first step is the reduction to hydroquinone, followed by a loss of methanol. This reaction fa-... 

Macke recently introduced a monoreactive DOTA prochelator (4,7,10-tricarboxymethyl-tert-butyl ester A, A, A", A "-tetraazacyclododecane-1 -acetate), which was coupled to Tyr3—Lys5 (BOQ-octreotide via solid-phase peptide synthesis. A one-step deprotection reaction generated the bioactive compound DOTATOC in about 65% yield.142 The 90Y and 177Lu DOTATOC complexes have shown promise for the treatment of neuroendocrine tumors in early clinical trials.143,444... 

The physicochemical and other properties of any newly identified drug must be extensively characterized prior to its entry into clinical trials. As the vast bulk of biopharmaceuticals are proteins, a summary overview of the approach taken to initial characterization of these biomolecules is presented. A prerequisite to such characterization is initial purification of the protein. Purification to homogeneity usually requires a combination of three or more high-resolution chromatographic steps (Chapter 6). The purification protocol is designed carefully, as it usually forms the basis of subsequent pilot- and process-scale purification systems. The purified product is then subjected to a battery of tests that aim to characterize it fully. Moreover, once these characteristics have been defined, they form the basis of many of the QC identity tests routinely performed on the product during its subsequent commercial manufacture. As these identity tests are discussed in detail in Chapter 7, only an abbreviated overview is presented here, in the form of Figure 4.5. 

Nylen, R. A. (2000). The Ultimate Step-By-Step Guide to Conducting Pharmaceutical Clinical Trials in the USA. RAN Institute, Tampa, FL. 

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