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Week 6 Chapter 10 Welcome to Week 6 Starting week six video Please watch the online video (52 seconds). OPTIONAL‐Please participate in the online discussion forum. Chapter 10 ‐ Lead Discovery Introduction to Chapter 10 Chapter 10 contains seven subsections. In Vitro Screening Fragment Based Screening Filtering Hits Pt 1 Filtering Hits Pt 2 Filtering Hits Pt 3 Selective Optimization of Side Activities Natural Products Upon completing this chapter, you understand the different methods for screening libraries of molecules for hits. You should also understand how to prioritize the hits as leads for their potential for ultimately becoming drugs. Finally, you should realize that some leads are discovered through non‐screening approaches. OPTIONAL‐Please participate in the online discussion forum. 10.1 In Vitro Screening Finding hits video Please watch the online video (6 minutes, 34 seconds). A condensed summary of this video can be found in the Video summary page. OPTIONAL‐Please participate in the online discussion forum. In silico screening Background: The most common method for finding hits involves searching through libraries of molecules using high‐throughput screening to reveal compounds with promising activity. Instructions: Read the passage below on screening molecules through computer modeling. Learning Goals: To understand the advantages and limitations of screening molecules through computer simulations. The rise in computing power, especially in the area of protein modeling, allows library compounds to be “flown” into a binding pocket that is modeled on a computer. Binding energies can then be estimated to approximate the Ki of each library member. The process of matching a compound to a binding site is called docking. Estimating the binding energy is called scoring. The overall process of testing for biological activity using a computer simulation is called in silico screening or virtual screening. A very attractive aspect of in silico screening is that the library does not to be real. As long as one can draw the molecules in a computer, the computer will handle docking the molecule to the target protein. The logistics of obtaining, maintaining, and dispensing compounds for testing are unnecessary. The library can potentially be far larger than the one or two million compound libraries held by major pharmaceutical companies. While there are many attractive aspects to in silico screening, the method is still developing. One problematic aspect is scoring. Current scoring methods are somewhat inaccurate, and many compounds that are not strong binders are predicted to be hits. The number of false hits, or false positives, can be reduced by using multiple different scoring methods. Only compounds that are predicted to be active through multiple methods are selected as hits. This approach is called consensus scoring. The activity of any virtual hits must be confirmed by synthesizing a sample of the molecule and testing the compound in an in vitro screen. OPTIONAL‐Please participate in the online discussion forum. 10.2 Fragment Based Screening Fragment‐based screening video Please watch the online video (7 minutes, 58 seconds). A condensed summary of this video can be found in the Video summary page. OPTIONAL‐Please participate in the online discussion forum. Revisiting stromelysin inhibitors Background: Fragment based drug discovery involves screening for small, weakly active compounds in an assay and then tethering them together to form hit‐level compound. Instructions: Read the passage below concerning a fragment based search for inhibitors of stromelysin, a topic that was first presented in Chapter 9. Use the ideas in the passage to answer the questions that follow. Learning Goal: To understand better the types of molecules used as fragments and how hits are generated from the fragments. Back in Chapter 9 we discussed the development of stromelysin inhibitors to highlight the relationship between binding energies and the structure of a molecule. As it so happens, the stromelysin study is also an example of fragment based drug discovery. The study started with two fragments that were found to bind to stromelysin. One was acetohydroxamic acid (1) and the other was 4‐hydroxybiphenyl (2). Note that both are small, fragment‐sized molecules and have weak Ki values close to 1 mM and binding energies of −2.4 and −4.8 kcal/mol, respectively. The fragments were then joined together. In this particular study, the binding sites for 1 and 2 were known to be close together and the approximate orientations of the two fragments were also known. These two details greatly helped the research team in designing tethers to connect the two fragments. The team reported four different tethers through the addition of between one and four CH2 units. The discussion becomes more complicated because the activities are reported as IC50 values instead of Ki values. Through the Cheng‐Prussoff equation, if we know the concentration of the substrate ([S]) and Km of the substrate for stromelysin, we can convert the IC50 values to Ki values, which can in o turn be used to calculate ΔG bind of the tethered fragments. The original stromelysin report gives [S] as 200 µM. Km is not provided, but a very similar enzyme o has a Km of 4,000 µM for stromelysin. Using these values, we can determine Ki and ΔG bind for the best hit formed by tethering fragments. The most potent hit is compound 4. o An interesting thing about fragment binding is that Ki and ΔG bind of hit can be determined based on the fragments that were combined. Specifically, if fragments 1 and 2 are correctly combined to form a new hit, then Ki of the hit should be equal to the product of the Ki values of the two fragments. Ki (hit) = Ki (fragment 1) × Ki (fragment 2) o o Similarly, ΔG bind of the hit should be equal to the sum of the ΔG bind of the two fragments. o o o ΔG bind (hit) = ΔG bind (fragment 1) + ΔG bind (fragment 2) −6 −3 −3 Under this logic, Ki of compound 4 should be 4.8×10 M (17×10 × 0.28×10 = the product of the −6 o two fragment Ki values). Instead, the actual value is 0.30×10 M. ΔG bind of compound 4 should be −7.2 kcal/mol (−2.4 + −4.8). Instead, the actual value is −8.9 kcal/mol. Compound 4 (the hit) binds more strongly than we would predict based on its fragments. Why do the predictions (which are theoretically sound) differ from the experimental value? The discrepancy is the tether. Compound 4 has two more CH2 groups than the individual fragments. These CH2 groups lie within a channel in the protein and generate binding energy through the hydrophobic effect. In Chapter 9, the binding energy of a CH2 group through the hydrophobic effect was listed as 0.8 kcal/mol. The energy difference between 4 and the fragments is 1.7 kcal/mol, essentially equal to 2 × 0.8. Once we consider the effect of the additional CH2 groups, the strong binding of hit 4 is more reasonable. Please complete the online exercise. OPTIONAL‐Please participate in the online discussion forum. 10.3 Filtering Hits Pt 1 Visual inspection video Please watch the online video (8 minutes, 33 seconds). A condensed summary of this video can be found in the Video summary page. OPTIONAL‐Please participate in the online discussion forum. Another structural alert Background: Compounds that contain functionality that may cause toxicity problems raise a structural alert. Anilines are perhaps the most common functional group that causes structural alert. Instructions: Read the passage below about arylacetic acids, which also trigger a structural alert. Learning Goal: To gain exposure to another functional group that forms reactive metabolites and is associated with structural alerts. Please continue on the following page… In addition to anilines, arylacetic acids (1) frequently form reactive metabolites. Like most carboxylic acids, arylacetic acids often undergo phase II metabolism and are conjugated with glucuronic acid (2). Glucuronides of arylacetic acids can rearrange from the 1‐glucuronide (3) to the 3‐ glucuronide (4). The 3‐glucuronide exists in equilibrium with its open‐chain form (5). The open‐ chain form is important because it can react with the NH2 groups on lysine residues of proteins, and ultimately the glucuronide becomes covalently bound to the protein through a multistep process. Modified proteins can trigger an immune response and cause tissue damage. Tissue damage in the liver is particularly common because glucuronidation occurs primarily in the liver. Skin rashes can also indicate immune response problems. OPTIONAL‐Please participate in the online discussion forum. Picking out problem compounds Background: Functional groups in a hit can cause the hit to be less attractive to a drug discovery program. Being able to visually identify problematic compounds can help a medicinal chemistry group to advance the correct compounds to the lead optimization stage. Instructions: Look at the structures below and answer the questions that follow. Learning Goal: To practice identifying compounds that contain less desirable functional groups. Below are six hit‐like compounds. Please complete the online exercise. OPTIONAL‐Please participate in the online discussion forum. 10.4 Filtering Hits Pt 2 Molecular indexes video Please watch the online video (5 minutes 15 seconds). A condensed summary of this video can be found in the Video summary page. OPTIONAL‐Please participate in the online discussion forum. Ligand efficiency calculations Background: Of the many molecular indexes, ligand efficiency (LE) is among the most widely used. Instructions: Use the equation above to calculate the typical LE values of hits and drugs. Furthermore, use the equation to calculate the LE value of two drugs, duloxetine and sildenafil. Learning Goal: To gain a sense of typical values for LE for different types of compounds relevant to drug discovery.
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