Compound Optimization

Extensive pharmacology Mutagenicity Structural optimization Backup compounds Optimal agent selection... 

Compound optimization in early- and late-phase drug discovery is covered, emphasizing physicochemical properties, in vitro absorption, metabolism and in vivo animal pharmacokinetic methodologies. 

Compound optimization, to screen a series of therapeutic diug candidates to find the compounds that are most specific for the target protein and those that cause unintended effects, i.e. improved understanding of the molecular mode of action including structure-activity relationships for on-target versus off-target effects... 

Colony-stimulating Factors Combinatorial Chemistry Compartment Competitive Antagonists Complement System Complement-type Repeat Complex Disease Compound Libraries Compound Optimization Computational Biology Computerized Tomography COMT... 

At present, intercalation compounds are used widely in various electrochemical devices (batteries, fuel cells, electrochromic devices, etc.). At the same time, many fundamental problems in this field do not yet have an explanation (e.g., the influence of ion solvation, the influence of defects in the host structure and/or in the host stoichiometry on the kinetic and thermodynamic properties of intercalation compounds). Optimization of the host stoichiometry of high-voltage intercalation compounds into oxide host materials is of prime importance for their practical application. Intercalation processes into organic polymer host materials are discussed in Chapter 26. 

In this chapter we described the thermodynamics of enzyme-inhibitor interactions and defined three potential modes of reversible binding of inhibitors to enzyme molecules. Competitive inhibitors bind to the free enzyme form in direct competition with substrate molecules. Noncompetitive inhibitors bind to both the free enzyme and to the ES complex or subsequent enzyme forms that are populated during catalysis. Uncompetitive inhibitors bind exclusively to the ES complex or to subsequent enzyme forms. We saw that one can distinguish among these inhibition modes by their effects on the apparent values of the steady state kinetic parameters Umax, Km, and VmdX/KM. We further saw that for bisubstrate reactions, the inhibition modality depends on the reaction mechanism used by the enzyme. Finally, we described how one may use the dissociation constant for inhibition (Kh o.K or both) to best evaluate the relative affinity of different inhibitors for ones target enzyme, and thus drive compound optimization through medicinal chemistry efforts. 

Quantitative assessment of enzyme affinity for various members of these chemical series is critical for development of a meaningful understanding of SAR and ultimately for compound optimization for clinical use. 

If the inhibitor is found to bind rapidly (linear progress curves) and dissociate rapidly (rapid recovery of activity upon dilution) from its target enzyme, then one can proceed to analyze its inhibition modality and affinity by classical methods. The modes of reversible inhibition of enzymes were described in Chapter 3. In the next section of this chapter we will describe convenient methods for determining reversible inhibition modality of lead compounds and lead analogues during compound optimization (i.e., SAR) studies. 

An important point to realize here is that attempts to quantify the relative potency of irreversible enzyme inactivators by more traditional parameters, such as IC50 values, are entirely inappropriate because these values will vary with time, in different ways for different compounds. Hence the SAR derived from IC50 values, determined at a fixed time point in the reaction progress curve, is meaningless and can be misleading in terms of compound optimization. Unfortunately, the literature is rife with examples of this type of inappropriate quantitation of irreversible inactivator potency, making meaningful comparisons with literature data difficult, at best. 

The great power of mechanistic enzymology in drug discovery is the quantitative nature of the information gleaned from these studies, and the direct utility of this quantitative data in driving compound optimization. For this reason any meaningful description of enzyme-inhibitor interactions must rest on a solid mathematical foundation. Thus, where appropriate, mathematical formulas are presented in each chapter to help the reader understand the concepts and the correct evaluation of the experimental data. To the extent possible, however, I have tried to keep the mathematics to a minimum, and instead have attempted to provide more descriptive accounts of the molecular interactions that drive enzyme-inhibitor interactions. 

The productivity of pharmaceutical R D in proportion to spend has fallen substantially to the point that its very existence is under threat [1,2]. It has been estimated that 93-96% of nominated candidate drugs fail at some stage in development [3] all attrition, from the start of compound optimization in the discovery phase of a project to launch on the market, is probably closer to 99%. Therefore, there is an urgent imperative to directly address the reasons for attrition, which from candidate drug nomination onwards can be divided into three broad categories [3-5] ... 

The following protocol describes a general method for using bis-NHS ester PEG compounds. Optimization of concentrations should be done for each application to assure the best possible results. See also the protocol in Chapter 28, Section 1, which describes the use of homobifunctional... 

Double Silylation of Aliphatic Nitro Compounds-Optimization and Procedures Double silylation of AN with standard silylating agents 5/X/Base is mechanistically shown in Chart 3.20 as a four-step process. 

Lloyd et al.1 described automation processes for compound optimization and simultaneous implementation of (1) a LIMS system to automate and track the flow of sample information, data analysis, and reporting (2) an automated data archiving system to handle a large number of LC/ MS/MS data files (3) custom software to track a large number of protocol flows and (4) workstation automation. 

Lloyd, T.L. et al. 2006. Laboratory automation for compound optimization and early development drug metabolism A Wyeth case study. Am. Drug Discov. 1 27. 

The yields and relative amounts of products greatly depend on proportions of requisite starting compounds. Optimal yields of the acylated products are obtained using a 1 1 1 ratio of the reactants, whereas best yields of cyanine dyes are afforded when 1 1 2 molar ratios are used. Using the latter ratio, the yields of acylation products 50 are in the range 8.2-78.7% while those for the cyanines 51 are 0.5-63.8%. This acylation offers a route to cyanine dyes of the tetrazoloisoindole series with varying R groups. 

Caldwell, G. W Ritchie, D. M. Masucci, J. A. Hageman, W Yan, Z. The new pre-preclinical paradigm compound optimization in early and late phase drug discovery. Curr Top Med Chem 2001, 1, 353-366. 

What makes prediction of drug elimination complex are the multiple possible pathways involved which explain why there is no simple in vitro clearance assay which predicts in vivo clearance. Because oxidative metabolism plays a major role in drug elimination, microsomal clearance assays are often used as a first line screen with the assumption that if clearance is high in this in vitro assay it is likely to be high in vivo. This assumption is often, but not always true because, for example, plasma protein binding can limit the rate of in vivo metabolism. However, compounds which have a low clearance in hepatic microsomes can be cleared in vivo via other mechanisms (phase II metabolism, plasmatic errzymes). Occasionally, elimination is limited by hepatic blood flow, and other processes like biliary excretion are then involved. The conclusion is that the value of in vitro assays needs to be established for each chemical series before it can be used for compound optimization. 

Once a lead compound has been identified by one of the techniques described above and the decision has been made to optimize this compound, the next task is to determine an approach to compound optimization. Typically, this approach is achieved via a two-step strategy ... 

Despite these challenges, the area of K+ channel openers (PCOs) is emerging as an active area of drug design. Over the past 5-10 years, eight novel structural classes of PCOs have received systematic development benzopyrans (e.g., cromakalim, 7.27), cyanoguanidines (e.g., pinacidil, 7.28), thioformamides (e.g., aprikalim, 7.29), pyridyl nitrates (e.g., nicorandil, 7.30), benzothiadiazines (e.g., diazoxide, 7.31), pyrimidine sulphates (e.g., minoxidil sulphate, 7.32), tertiary carbinols, and dihydropyridines. These various classes have been subjected to analog preparation with compound optimization via structure-activity studies. 

Molteni, L. 1982. Effects of the polysaccharidic carrier on the kineticfate of drugs linked to dextran and inulin in macromolecular compounds. Optimization of Drug Delivery, edited by H. Bundgaard.A. B. Hansen, and H. Kofod, 285-300. Copenhagen Munksgaard. 

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