Download Welcome to Week 6 Chapter 10 - Lead Discovery 10.1 In Vitro

 WelcometoWeek6
Startingweeksixvideo
Please watch the online video (52 seconds). OPTIONAL‐Please participate in the online discussion forum. Chapter10‐LeadDiscovery
IntroductiontoChapter10
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. 10.1InVitroScreening
Findinghitsvideo
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. Insilicoscreening
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. 10.2FragmentBasedScreening
Fragment‐basedscreeningvideo
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. Revisitingstromelysininhibitors
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 turn be used to calculate ΔGobind of the tethered fragments. The original stromelysin report gives [S] as 200 µM. Km is not provided, but a very similar enzyme has a Km of 4,000 µM for stromelysin. Using these values, we can determine Ki and ΔGobind for the best hit formed by tethering fragments. The most potent hit is compound 4. An interesting thing about fragment binding is that Ki and ΔGobind 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) Similarly, ΔGobind of the hit should be equal to the sum of the ΔGobind of the two fragments. ΔGobind (hit) = ΔGobind (fragment 1) + ΔGobind (fragment 2) Under this logic, Ki of compound 4 should be 4.8×10−6 M (17×10−3 × 0.28×10−3 = the product of the two fragment Ki values). Instead, the actual value is 0.30×10−6 M. ΔGobind 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. 10.3FilteringHitsPt1
Visualinspectionvideo
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. Anotherstructuralalert
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. Pickingoutproblemcompounds
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. 10.4FilteringHitsPt2
Molecularindexesvideo
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. Ligandefficiencycalculations
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. Please complete the online exercise. OPTIONAL‐Please participate in the online discussion forum. Ligandlipophilicityefficiency
Background: Molecular indexes and metrics allow members of a drug discovery team to quickly prioritize one hit above another. Instructions: Read the passage below on another metric, ligand lipophilicity efficiency, by which hits may be prioritized. Use the information on ligand lipophilicity efficiency to answer the questions that follow. Learning Goal: To learn another molecular index by which the quality of a hit can be gauged. A frequently encountered molecular index is ligand lipophilicity efficiency, or LLE.1 (Do not confuse LLE with LE, the visually similar but very different ligand efficiency.) LLE is the difference between a drug's activity in the form of −log IC50 or −log Ki and a drug's lipophilicity in the form of log P. A more highly positive value for LLE is more favorable. LLE acknowledges the reality that additional activity (a larger value for −log IC50.) is obtained by adding molecular weight and likely increasing lipophilicity (a larger value for log P). According to Lipinski's rule, log P has a maximum value of 5. Therefore, hits with a value for log P closer to 5 and only hit‐like activity have low values for LLE leave little room in terms of lipophilicity for growth of the molecule. LLE creates a direct relationship between activity and lipophilicity. A more active molecule will have a larger, positive value for −log IC50. 1. Edwards, M. P.; Price, D. A. Role of Physiochemical Properties and Ligand Lipophilicity Efficiency in Addressing Drug Safety Risks. Annu. Rep. Med. Chem. 2010, 45, 2615‐2623. Please complete the online exercise. OPTIONAL‐Please participate in the online discussion forum. 10.5FilteringHitsPt3
Otherleadselectioncriteriavideo
Please watch the online video (7 minutes 59 seconds). A condensed summary of this video can be found in the Video summary page. OPTIONAL‐Please participate in the online discussion forum. Leadsarebetterthanhits
Background: Hits are often defined as compounds with moderate activity (Ki = 1 µM) against a target. The most promising hits will be advanced as leads. Instructions: Read the passage below on how a hit is structurally modified and improved in activity during the lead selection process. Learning Goal: To make clear that a hit is typically tweaked and improved before it becomes a lead. Back in the 1970s, Saturday morning cartoons for children on ABC (a United States television network) were broadcast with occasional educational programming called Schoolhouse Rock. The short Schoolhouse Rock cartoons taught about topics like grammar and history. One particular cartoon was called I'm Just a Bill and followed a congressional bill through the long and winding approval process of becoming a federal law. Only after significant modification and editing can a bill be approved. At the close of the cartoon, the US President approves the bill. The bill responds by shouting, "Oh yes!" A hit is very much like a congressional bill. A hit merits investigation because it shows activity against a target, but that credential alone does not make the hit worthy of being a lead. The hit is put through a battery of tests ‐ the filtering process that we discussed in sections 10.3, 10.4, and 10.5 ‐ to make sure that only the most promising compounds are advanced as leads. Once a lead, a molecule will require more and more resources from the pharmaceutical company. One aspect of lead discovery that we have not discussed is how the structure of a hit is explored during the lead discovery stage. Chemists are not idle during this process, and simple structural modifications to the hit will be made to confirm that there is room for improving the binding of the molecule to its intended target. The outcome of these modifications is a series of molecules very similar to the original hit. It is likely one of these modified hits that will ultimately be advanced as the lead compound. Therefore, it is common for a lead molecule to bind more strongly to the target than the original hit. Leads will often have Ki values of 100 nM or even lower ‐ a marked improvement over the typical value of 1 µM of a hit. Just as a bill, if it becomes a law, is modified and crafted as it is marched through the approval process, so also a hit is incrementally improved as it is becoming judged to be a lead. (Oh yes!) OPTIONAL‐Please participate in the online discussion forum. 10.6SelectiveOptimizationofSideActivities
SOSAvideo
Please watch the online video (6 minutes 41 seconds). A condensed summary of this video can be found in the Video summary page. OPTIONAL‐Please participate in the online discussion forum. Casestudy:viloxazine
Background: SOSA ‐ selective optimization of side activities ‐ is an alternative to traditional lead discovery techniques. In SOSA, a compound that is intended to bind to one target is modified so that the compound instead binds to a different target. Instructions: Read the passage below about viloxazine, a drug that was discovered through the SOSA approach. Learning Goal: To gain exposure to another SOSA drug. Many treatments for high blood pressure have been developed. β‐Blockers are a class of blood pressure medication developed in the 1960s and antagonize (block) β‐receptors, which play a role in regulating heart rate and blood vessel dilation. Propranolol (1) is among the most frequently prescribed β‐blockers. β‐Blockers also display sedative and anticonvulsant activity. In order to exploit the secondary sedative effects of β‐blockers, researchers modified the pharmacophore of β‐blockers (2) by forming a new ring (3) with the flexible side chain. The rationale was that restricting the conformation of the molecule would enhance binding to secondary targets will minimizing binding to β‐receptors. The formation of rings is a standard approach for controlling the conformation of a molecule. The ultimate outcome of the research program was viloxazine (4), an antidepressant that blocks the uptake of certain neurotransmitters from synaptic junctions. Image credit: Pearson Education Viloxazine is therefore an example of SOSA drug. Viloxazine was designed by the modification of an existing compound. The modification were specifically intended to improve side activities and diminish binding to the original target. OPTIONAL‐Please participate in the online discussion forum. SOSAandmolecularindexes
Background: SOSA is potentially excellent method for discovering leads by screening established drugs against new targets. SOSA hits are typically safe and show good cell permeability. Instructions: Answer the questions below about how SOSA hits might fare when evaluated against the molecular indexes discussed in this chapter. Learning Goal: To apply the ideas of filtering hits to the active molecules identified in a SOSA based screen. Please complete the online exercise. OPTIONAL‐Please participate in the online discussion forum. 10.7NaturalProducts
Naturalproductsvideo
Please watch the online video (6 minutes 50 seconds). A condensed summary of this video can be found in the Video summary page. OPTIONAL‐Please participate in the online discussion forum. Morenaturalproducts
Background: Natural products are an established source for hits, leads, and drugs in a drug discovery program. Instructions: Read the passage below on three drugs that were developed as a result of insights gained from related natural products. Learning Goal: To recognize the structural similarities between certain drugs and the natural products that resulted in their discovery. atorvastatin The synthesis of cholesterol by the body requires conversion of 3‐hydroxy‐3‐methylglutaryl‐CoA (1, HMG‐CoA) to mevalonic acid (2) by HMG‐CoA reductase. This fact was known in the early 1970s. During the 1970s, scientists at several pharmaceutical companies investigated different fungi for compounds that might inhibit HMG‐CoA reductase and therefore shut down cholesterol biosynthesis. The first marketed compound from this search was lovastatin (3), which is found in oyster mushrooms. Once hydrolyzed in the body, the resulting acid (4) bears a clear resemblance to mevalonic acid (2) (see boxed part of molecule). Armed with the idea of attaching a non‐polar group to mevalonic acid as a lead compound (5), drug companies sought to develop synthetic versions of lovastatin. The first synthetic version was atorvastatin (6), which is better known as Lipitor. zidovudine Just as amino acids are linked together to make proteins, nucleosides are connected to make the biological molecules DNA and RNA. The enzymes that make DNA or RNA are called nucleotide polymerases. Nucleosides consist of two parts. One part is a sugar, either ribose (7) or 2‐
deoxyribose (8). The other part is a nucleobase. Nucleobases can vary in structure but consist of either a bicyclic purine core (9) or a monocyclic pyrimidine core (10). Specific examples of nucleosides are shown below. Nucleosides play a vital role in certain diseases, including viral infections. A cell infected by a virus is converted into a virus factory. One thing the cell produces is the DNA or RNA required to make a new virus. Naturally, to make viral DNA or RNA, a cell needs a steady supply of nucleosides. A method for treating viral infections is to dose a patient with nucleoside analogues. Nucleoside analogues are molecules that are similar enough to natural nucleosides to serve as a substrate for nucleotide polymerases but lack complete functionality to form active DNA or RNA. The overall effect of nucleoside analogues is that they slow the rate of virus production by infected cells. The first nucleoside analogue developed to treat HIV infections was zidovidine (13). Zidovudine very closely resembles the structure of 2'‐deoxythymidine (12), a naturally occurring nucleoside in the body. Other antiviral nucleoside analogues include aciclovir (14) and telbivudine (15). Nucleoside analogues walk a very fine line for their activity. The compounds must resemble natural nucleosides closely enough to bind the active site of nucleotide polymerases. The compounds must also be different enough not to engage in the chain elongation process of making DNA or RNA. propranolol β‐adrenergic receptors affect a person's heart rate and vasoconstriction. Epinephrine (16) is an endogenous ligand for the β‐adrenergic receptors. When pharmaceuticals started researching β‐
adrenergic receptor antagonists as potential blood pressure therapies, epinephrine was a natural starting point. The first β‐adrenergic receptor antagonist, called a β‐blocker, was dichloroisoprenaline (17). Dichloroisoprenaline was not sufficiently active in humans, but continued research in the field afforded propranolol (18), the first successful β‐blocker. Both dichloroisoprenaline and propranolol have obvious structural similarities to epinephrine. OPTIONAL‐Please participate in the online discussion forum.