Mentoring an Undergraduate Research Student in the Structural and

Dec 1, 2006 - Department of Chemistry, Pennsylvania State University, Lehigh Valley Campus, Fogelsville, PA 18051. J. Chem. Educ. , 2006, 83 (12), p 1...
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In the Classroom

Mentoring an Undergraduate Research Student in the Structural and Nonstructural Properties of Drugs Julie B. Ealy* and Veronica Kvarta Department of Chemistry, Pennsylvania State University, Lehigh Valley Campus, Fogelsville, PA 18051; *[email protected]

Undergraduate research offers the student an opportunity to take ownership of their scientific knowledge. However, the time challenge of undergraduate research has been noted by many instructors (1–4). It is particularly challenging at a small commuting campus of a major university where full-time science majors are most likely working 20–30 hours per week outside of school and the instructor teaches the chemistry lecture and lab sections without technical assistance. Although with significant constraints, it is still possible for substantial research to take place even when the students remain at the campus just two years and have completed only one year of chemistry and biology. A recent research topic was the nonstructural and structural properties of drugs. The 3D structural importance of drugs, their importance in disease, and the use of computers to view 3D molecular images have applications in many science disciplines: (i) General chemistry students are introduced to molecular structure and the 3D importance of molecules (5). (ii) One focus of an organic chemistry course is how functional groups affect the distribution of molecules in the body and possible products of the metabolism of pharmaceuticals and environmental toxins (6). (iii) An organic chemistry course provides students with an opportunity to observe the structure of DNA and the interactive binding of oxygen to a heme molecule (7). (iv) Biochemistry introduces students to enzymes and interactions of molecules with the active site of enzymes. There are numerous applications to the 3D structure of drugs and their fit in the active site of enzymes. This is an area of importance in the development of drugs (8– 11). (v) An interest in personal health should encourage us to be knowledgeable about diseases, drugs, and their side effects (12). (vi) High school teachers’ knowledge about pharmacology will help them to answer their students’ questions about drugs (13–14). The coronavirus that causes SARS, severe acute respiratory syndrome (15), was researched by the primary author with an undergraduate student in prepara-

Table 1. Characteristics of the 31 Drugs Characteristic

Value

Average molecular weight

512.36 g/mol

Average number of rings per molecule

3

Percent of rings that were benzene

33.60%

Average percent of compounds with at least one OH group

54.50%

Average percent of compounds with at least one C=O group

63.60%

Average percent of compounds with at least one N in a ring structure

60.60%

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tion for teaching a first-year seminar course on SARS. The viral main proteinase, an enzyme that functions to ensure cleavage of the enzymes so the coronavirus completes its replication, is targeted for drug development. The 3D shape of the enzyme made it evident that the nonstructural and structural properties of drugs were important in the treatment of disease. These properties of drugs became the focus of research with another undergraduate student, which is the research emphasis of this article. Questions To Be Addressed There were several questions to be answered regarding drugs: • What are the structural and nonstructural characteristics of drugs? • Are these characteristics common among drugs? • What is important about the characteristics?

Where To Begin? We began our research by reading an article, “Recent Development in Cheminformatics and Chemogenomics” (16). The main purpose of this particular article was to acquaint the student researcher with the need to carefully read primary literature and not be satisfied with a cursory understanding of newly encountered scientific terms. It was important that she realize a simple definition of a term was not sufficient if she was to understand the interconnections within later articles. Secondarily, the article acquainted us with terminology that we would encounter in later articles such as QSAR (quantitative structure activity relationship), Lipinski’s rule of five, pharmacophore, high throughput screening, logP, and lipophilicity, to name just a few. Any term that was not clearly understood was researched and discussed in the context of the article. Once the groundwork was established by reading primary literature, an article from Annual Reports in Medicinal Chemistry (17) was chosen next. This article, “To Market, To Market”, provided information about 31 drugs that had reached the market with accompanying information showing the 2D structure, molecular weight, name, disease or condition treated, method of administration, and general mode of action. The primary author of this article compiled a general list of structural characteristics that seemed to stand out for many of the drugs. The student researcher separately made a table of characteristics of the 31 drugs and then we met to discuss and compare the results. Table 1 illustrates the more prevalent characteristics that were used for analysis of the 31 drugs.

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In the Classroom Table 2. Structural and Nonstructural Characteristics of Drugs Structural

Nonstructural

Hydrogen-bond donor a

Molecular weight a

Hydrogen-bond acceptor

Log P a

a

Rotatable bonds Number of rings Common frameworks a

Lipinski’s rule of five (22).

carbonyl group C O

NH2

O

N

N

O N

N

O

nitrogen in a ring

O

P

O

O

O O

Figure 1. Structure of adefovir dipivox.

benzene ring O

OH

O CH3 NH2 HCl

O

OH

OH group

O

O OH OH

Figure 2. Structure of amrubicin hydrochloride.

Two of the molecules analyzed are shown in Figures 1 and 2. These molecules illustrate the characteristics from our analysis. The two-dimensional images in this manuscript were drawn with ISIS Draw; two-dimensional images for drugs can also be obtained from the Chemidplus Web site (18). Comparison of Our Analysis with Other Studies At the completion of our analysis we questioned whether our results were illustrative of the majority of drugs on the market. We looked at articles (19–23) that described “druglike” molecules. We define “druglike” molecules to be molecules containing structural and nonstructural properties consistent with the majority of known drugs. Walters and Murcko (19) examined studies involving over 5000 com-

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pounds. After comparing the results of the different studies regarding “druglike” characteristics, we compiled a list of structural and nonstructural characteristics shared among the compounds (Table 2). Our analysis of 31 compounds had some similarities with the 5000 compounds of Walters and Murcko (19) such as number of rings, molecular weight, and the presence of hydrogen-bond donors and acceptors though we did not look at the 31 compounds with donors and acceptors in mind. We had focused our initial analysis on structural characteristics and included molecular weight as a nonstructural characteristic. Structural features of drugs influence molecular size, flexibility of a molecule, and atoms that could be hydrogenbond acceptors or donors. Consequently, the structure of a drug affects its activity. Nonstructural and structural characteristics and common frameworks of drugs will be considered below.

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Nonstructural Characteristics Molecular Weight An important question is, “What is an acceptable molecular weight of a drug?” According to Lipinski (22) the molecular weight of a medicinal drug should be less than 500 g兾mol (or 500 Da; daltons are units used by biologists for proteins of high molecular weight). Our analysis of 31 drugs revealed a weight greater than 500 g兾mol for eleven of the drugs with five of them having a weight greater than 600 g兾mol. Drugs with a molecular weight over 600 g兾mol fell into categories of antithrombotic, anticancer (2), antifungal, and immunosuppressant. The number of drugs with molecular weights over 500 g兾mol contributed to our group of 31 having an average molecular weight greater than 500 g兾mol. The weight of a drug is important because an increase in the molecular weight of a drug results in poorer intestinal and blood–brain barrier permeability. It also takes more time for a drug to permeate the lipid bilayer as molecular weight increases. Some orally active drugs that exceed a molecular weight of 500 g兾mol are antibiotics, antifungals, vitamins, and cardiac glycosides. They retain their oral bioavailability because they possess groups that act as substrates for transporters. Log P Log P is not a characteristic that showed up directly in our analysis; however, its relevance became important as we progressed further into our research. The partitioning equilibrium constant, P, for a molecule is the ratio of the concentration of a drug in octanol compared to its concentration in water. P can be determined by putting a specific amount of drug in equal quantities of octanol and water. The mixture is shaken, the layers partition, and then the layers are separated. The concentration of drug in each layer is then determined experimentally, often using spectroscopy. A negative log P indicates that a drug is hydrophilic, having an affinity for water, while a positive log P indicates lipophilicity, having an affinity for fat.

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In the Classroom

Table 3. Moriguchi’s Parameters for log P Parameter

H or L

General Description

CX

L

Sum of C and halogen atoms

NO

H

Total number of N and O atoms

PRX

L

Proximity effect of N and O

Table 4. Calculated and Experimental log P log P

Drug

Formula

Naloxone

C1 9 H2 1 NO4

1.64

2.09

C2 2 H2 6 N2 O4 S

2.77

2.70

Calc

Exp

UB

H

Total number of unsaturated bonds

Diltiazem

HB

L

Intramolecular H bonds

Ketoprofen

C1 6 H1 4 O3

POL

L

Aromatic polar substituents

Hydrochlorothiazide

C7 H8 ClN3 O4 S2

AMP

H

Amphoteric property

Caffeine

ALK

L

Alkane, alkene, cycloalkane, or cycloalkene

C8 H1 0 N4 O2

Piroxicam

C1 5 H1 3 N3 O4 S

᎑0.29

᎑0.05

Glycine

C2 H5 NO2

᎑3.41

᎑3.21

2.97

3.12

᎑0.95

᎑0.07

0.037

᎑0.07

RNG

L

Ring structures except benzene

QN

H

Quaternary N

Ibuprofen

C1 3 H1 8 O2

2.82

3.50

NO2

L

Number of nitro groups

C9 H8 O4

0.891

1.19

NCS

L

Isothiocyanate

Aspirin (acetylsalicyclic acid)

BLM

L

Presence of β-lactam

Omeprazole

C1 7 H1 9 N3 O3 S

1.26

2.23

NOTE: H is hydrophilic and L is lipophilic.

Lipinski (22) found that most drugs have a logP less than 5. Compounds that exceed a log P of 5 are often central nervous system drugs that are more lipophilic because the brain is basically lipid. A drug needs to be soluble enough in the blood to be transported, but there must also be uptake of the drug in a membrane. The balance of the transport and uptake are the essence of log P. Moriguchi (24) derived an equation, without experimentation, that can be used to calculate log P, as illustrated below:

log P = 1.244 (CX )

0 .6

− 0.145 (UB)

− 1.017 (NO)

0 .8

0.9

− 0.406 (PRX )

+ 0.511 (HB) + 0.268 (PO OL )

− 2.215 ( AMP) + 0.912 ( ALK ) − 0.392 (RNG) − 3.684 (QN ) + 0.474 (NO2 ) + 1.582 ( NCS) + 0.773 (BLM) − 1.041 A brief description of the parameters in Moriguchi’s equation are shown in Table 3. (For calculation of log P, consult the references in 24 and the QMPRPlus Web site; ref 25.) We used this equation to calculate log P for a random collection of drugs for which we had access to experimental logP values. There are computer programs that will calculate log P, but we did not have access to any of them. Also, calculation by hand was useful since it allowed us to closely examine the 3D structure of the molecules. Our calculated log P results compared with experimental results (26) are shown in Table 4. Moriguchi (24) indicated that his linear regression equation did not differentiate among geometrical isomers because it was not precise enough. When Moriguchi checked his equa-

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tion with 1230 compounds it accounted for 91% of the variance in experimental logP values. A comparison of our calculated log P values and the experimental log P values using a t-test indicated that the two values were not statistically different ( p = 0.34).

Structural Characteristics Hydrogen-Bond Donors and Acceptors According to Lipinski (22), there should be no more than five hydrogen-bond donors and no more than ten hydrogenbond acceptors. The sum of N’s and O’s provides a rough estimate of the hydrogen-bond acceptors in a molecule. Likewise the number of NH bonds and OH bonds provides information about hydrogen-bond donors. An increased number of hydrogen-bond acceptors and donors increases the hydrophilicity of a molecule. The molecule has more polar areas that make it more soluble in an aqueous medium, such as blood. The increased polarity reduces the molecule’s ability to cross the cell membrane. Crossing the cell membrane refers to a molecule either entering a cell or leaving a cell. For example, in drug metabolism, the breakdown product would exit via the cell membrane. Student researchers were taught to build the drugs using Spartan Pro, a program used to design three-dimensional structures of molecules by Wavefunction, Inc. (Irvine, CA). The 3D molecular structure provided a more realistic visual image of each drug than the 2D image. Students gained a better understanding of the structural characteristics of the drug, such as hydrogen-bond donors and acceptors. In addition, students learned how structure contributed to the calculation of log P. The 2D and 3D images of diltiazem are

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In the Classroom O

OH

CH3

O

S

N H N

O

S

CH3

N O

H3C

N

O

O

CH3 O

N CH3

Figure 4. Piroxicam: (top) 2D image and (bottom) 3D image.

drug’s overall size. This increase could help to fill a hydrophobic pocket at the receptor site resulting in a stronger binding of the drug to the target (27). The binding could be hindered, though, if the rings were connected by rotatable bonds making the drug too flexible. Figure 3. Diltiazem: (top) 2D image and (bottom) 3D image.

Common Frameworks A group of 5120 compounds was examined (20) and the compounds were broken down into common frameworks by removing all the side chains and considering all atoms and bonds to be equivalent. These 32 frameworks described the shape of half of the 5120 molecules examined. Seven of our 31 drugs fit into five of the common frameworks (Figure 5). The importance of the 32 different shapes representing half of the 5120 molecules is that the shapes can be used in widely divergent drug-design situations. Two of the drugs we analyzed are in the A framework (Figure 5). These drugs were fulvestrant, an anticancer drug, and norelgestromin, a contraceptive (Figures 6 and 7). Though they share a framework in common the additional side chains and groups contribute to their widely different functions as drugs.

shown in Figure 3 and the 2D and 3D images of piroxicam are shown in Figure 4. Rotatable Bonds and Number of Rings When there are too many rotatable bonds in a drug, the flexibility of the drug is increased and the drug can assume different conformations. Since the area of the cell receptor is relatively small, this can interfere with the potency of a drug and increase the time it takes for a drug to assume the correct geometry, interfering with its ability to bind properly in the receptor site. The addition of rings into the structure of a drug results in a change in the shape of a drug and increases the

A

B

C

2

2

D

1

E

1

1

Figure 5. Common frameworks of drugs (20). The number of our drugs representing a particular framework is shown below the framework.

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In the Classroom

Conclusion

OH CH3

Ketoprofen is used to illustrate the structural and nonstructural characteristics of our research. It is used for the relief of pain, tenderness, inflammation, and stiffness in arthritic patients. For ketoprofen’s nonstructural characteristics it has a molecular weight of 254 g兾mol making it an acceptable weight for its function as a drug. Its log P value of 3.12 indicates that it is lipophilic. Structurally there are two hydrogen-bond acceptors, one hydrogen-bond donor, five rotatable bonds, and two rings (Figure 8). Ketoprofen fits into category B (Figure 5), one of 32 frameworks (20). Our year of research was productive. The research student learned much about the properties of molecules that contribute to their role as drugs. It was obvious when she presented a paper at our regional undergraduate research conference she had taken ownership of the research. Later, at poster session of a local American Chemical Society meeting, she demonstrated mastery of the topic by the questions she answered for chemistry instructors at the session. Thus, it is possible to do research with an undergraduate who has completed a minimum number of science courses. It is also satisfying to work with young adults on a one-to-one basis and contribute to their knowledge base for future science courses.

H O H

F

H

F

S HO

F

F F

Figure 6. Structure of fulvestrant, typically used as an anticancer drug.

H3C

H

OH

CH

H H

H

N HO Figure 7. Structure of norelgestromin, a contraceptive.

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Figure 8. Structural characteristics of ketoprofen.

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