Revolutionizing Asthma Treatment: A Breakthrough in LABA Design Enhances Both Selectivity and Efficacy

Student: Sophia Liu
Table: MED10
Experimentation location: Home
Regulated Research (Form 1c): No
Project continuation (Form 7): No

Display board image not available



Abraha, D., Cho, S. H., Agrawal, D. K., Park, J. M., & Oh, C. K. (2004). (S,S)-Formoterol increases the production of il-4 in mast cells and the airways of a murine asthma model. International Archives of Allergy and Immunology, 133(4), 380-388.

Anderson, G., Linden, A., & Rabe, K. (1994). Why are long-acting beta-adrenoceptor agonists long-acting? European Respiratory Journal, 7(3), 569-578.

Baker, J. G., Proudman, R. G. W., & Hill, S. J. (2014). Salmeterol's extreme<i>β</i>2 selectivity is due to residues in both extracellular loops and transmembrane domains. Molecular Pharmacology, 87(1), 103-120.

Biava, H. B. (2023). CHE 202: Organic Chemistry II. LibreText.

Billington, C. K., Penn, R. B., & Hall, I. P. (2016). β2 agonists. Handbook of Experimental Pharmacology, 23-40.

Blom, M., & Sommers, De K. (1997). Comparative pharmacology of salmeterol and formoterol and their interaction with salbutamol in healthy volunteers. Clinical Drug Investigation, 14(5), 400-404.

Cazzola, M., Imperatore, F., Salzillo, A., Di Perna, F., Calderaro, F., Imperatore, A., & Matera, M. G. (1998). Cardiac effects of formoterol and salmeterol in patients suffering from COPD with preexisting cardiac arrhythmias and hypoxemia. Chest, 114(2), 411-415.

Cazzola, M., Matera, M. G., & Donner, C. F. (2005). Inhaled β2-Adrenoceptor agonists. Drugs, 65(12), 1595-1610.

Cazzola, M., Rogliani, P., & Matera, M. G. (2019). Ultra-LABAs for the treatment of asthma. Respiratory Medicine, 156, 47-52.

Coleman, R. A., Johnson, M., Niais, A. T., & Vardey, C. J. (1996). Exosites: Their current status, and their relevance to the duration of action of long-acting β2-adrenoceptor agonists. Trends in Pharmacological Sciences, 17(9), 324-330.

Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7(1).

Dhamane, M. V., Dhakane, P. A., & Merekar, S. A. (2023). In silico methods for drug designing and drug discovery. World Journal of Pharmaceutical and Medical Research, 9(5), 170-178.

Fahy, J. V. (2014). Type 2 inflammation in asthma — present in most, absent in many. Nature Reviews Immunology, 15(1), 57-65.

Freddolino, P. L., Kalani, M. Y. S., Vaidehi, N., Floriano, W. B., Hall, S. E., Trabanino, R. J., Kam, V. W. T., & Goddard, W. A. (2004). Predicted 3D structure for the human β2 adrenergic receptor and its binding site for agonists and antagonists. Proceedings of the National Academy of Sciences, 101(9), 2736-2741.

Global Asthma Network. (2022). The global asthma report 2022.

Global Initiative for Asthma. (2022). Global strategy for asthma management and prevention.

Gringauz, A. (1997). Introduction to medicinal chemistry: How drugs act and why. Wiley-VCH.

Hogg, J. C. (2004). Pathophysiology of airflow limitation in chronic obstructive pulmonary disease. The Lancet, 364(9435), 709-721.

Kuruvilla, M. E., Lee, F. E.-H., & Lee, G. B. (2018). Understanding asthma phenotypes, endotypes, and mechanisms of disease. Clinical Reviews in Allergy & Immunology, 56(2), 219-233.

Lambrecht, B. N., Hammad, H., & Fahy, J. V. (2019). The cytokines of asthma. Immunity, 50(4), 975-991.

Lee, Y., Warne, T., Nehmé, R., Pandey, S., Dwivedi-Agnihotri, H., Chaturvedi, M., Edwards, P. C., García-Nafría, J., Leslie, A. G. W., Shukla, A. K., & Tate, C. G. (2020). Molecular basis of β-arrestin coupling to formoterol-bound β1-adrenoceptor. Nature, 583(7818), 862-866.

Lötvall, J. (2001). Pharmacological similarities and differences between β2-agonists. Respiratory Medicine, 95, S7-S11.

Maison, N., Omony, J., Illi, S., Thiele, D., Skevaki, C., Dittrich, A.-M., Bahmer, T., Rabe, K. F., Weckmann, M., Happle, C., Schaub, B., Meyer, M., Foth, S., Rietschel, E., Renz, H., Hansen, G., Kopp, M. V., von Mutius, E., Grychtol, R., . . . Hose, A. (2022). T2-high asthma phenotypes across lifespan. European Respiratory Journal, 60(3), 2102288.

Masureel, M., Zou, Y., Picard, L.-P., van der Westhuizen, E., Mahoney, J. P., Rodrigues, J. P. G. L. M., Mildorf, T. J., Dror, R. O., Shaw, D. E., Bouvier, M., Pardon, E., Steyaert, J., Sunahara, R. K., Weis, W. I., Zhang, C., & Kobilka, B. K. (2018). Structural insights into binding specificity, efficacy and bias of a β2AR partial agonist. Nature Chemical Biology, 14(11), 1059-1066.

Naline, E., Zhang, Y., Qian, Y., Mairon, N., Anderson, G., Grandordy, B., & Advenier, C. (1994). Relaxant effects and durations of action of formoterol and salmeterol on the isolated human bronchus. European Respiratory Journal, 7(5), 914-920.

National Center for Biotechnology Information (2023). PubChem Compound Summary for CID 5152, Salmeterol. Retrieved September 8, 2023 from

National Center for Biotechnology Information (2023). PubChem Compound Summary for CID 3410, Formoterol. Retrieved September 8, 2023 from

Nurmagambetov, T., Kuwahara, R., & Garbe, P. (2018). The economic burden of asthma in the united states, 2008–2013. Annals of the American Thoracic Society, 15(3), 348-356.

Pavlidis, S., Takahashi, K., Ng Kee Kwong, F., Xie, J., Hoda, U., Sun, K., Elyasigomari, V., Agapow, P., Loza, M., Baribaud, F., Chanez, P., Fowler, S. J., Shaw, D. E., Fleming, L., Howarth, P. H., Sousa, A. R., Corfield, J., Auffray, C., De Meulder, B., . . . Fan Chung, K. (2018). "T2-high" in severe asthma related to blood eosinophil, exhaled nitric oxide and serum periostin. European Respiratory Journal, 53(1), 1800938.

Rasmussen, S. G. F., Choi, H.-J., Fung, J. J., Pardon, E., Casarosa, P., Chae, P. S., DeVree, B. T., Rosenbaum, D. M., Thian, F. S., Kobilka, T. S., Schnapp, A., Konetzki, I., Sunahara, R. K., Gellman, S. H., Pautsch, A., Steyaert, J., Weis, W. I., & Kobilka, B. K. (2011). Structure of a nanobody-stabilized active state of the β2 adrenoceptor. Nature, 469(7329), 175-180.

Ringdal, N., Derom, E., Wåhlin-Boll, E., & Pauwels, R. (1998). Onset and duration of action of single doses of formoterol inhaled via turbuhaler®. Respiratory Medicine, 92(8), 1017-1021.

The Ritedose Corporation. (2019, May). Highlights of prescribing information. U.S. Food and Drug Administration.

Rosenbaum, S. E. (2016). Basic Pharmacokinetics and Pharmacodynamics: An Integrated Textbook and Computer Simulations. John Wiley & Sons.

Roy, K. (2019). In silico drug design: Repurposing techniques and methodologies. Academic Press.

Santos, R., Ursu, O., Gaulton, A., Bento, A. P., Donadi, R. S., Bologa, C. G., Karlsson, A., Al-Lazikani, B., Hersey, A., Oprea, T. I., & Overington, J. P. (2016). A comprehensive map of molecular drug targets. Nature Reviews Drug Discovery, 16(1), 19-34.

Silverman, R. B., & Holladay, M. W. (2014). The organic chemistry of drug design and drug action (3rd ed.). Academic Press.

Sriram, K., & Insel, P. A. (2018). G protein-coupled receptors as targets for approved drugs: How many targets and how many drugs? Molecular Pharmacology, 93(4), 251-258.

Szlenk, C. T., GC, J. B., & Natesan, S. (2021). Membrane-Facilitated receptor access and binding mechanisms of long-acting <i>β</i>2-Adrenergic receptor agonists. Molecular Pharmacology, 100(4), 406-427.

Tanabe, T., Fujimoto, K., Yasuo, M., Tsushima, K., Yoshida, K., Ise, H., & Yamaya, M. (2007). Modulation of mucus production by interleukin-13 receptor α<sub>2</sub>in the human airway epithelium. Clinical & Experimental Allergy, 0(0), 071119182754008-???

Tiwari, D., & Gupta, P. (2021). Nuclear receptors in asthma: Empowering classical molecules against a contemporary ailment. Frontiers in Immunology, 11.

Tokmakova, A., Kim, D., Goddard, W. A., & Liggett, S. B. (2022). Biased β-Agonists favoring gs over β-Arrestin for individualized treatment of obstructive lung disease. Journal of Personalized Medicine, 12(3), 331.

van Noord, J., Smeets, J., Raaijmakers, J., Bommer, A., & Maesen, F. (1996). Salmeterol versus formoterol in patients with moderately severe asthma: Onset and duration of action. European Respiratory Journal, 9(8), 1684-1688.

Waldeck, B. (2002). β-Adrenoceptor agonists and asthma—100 years of development. European Journal of Pharmacology, 445(1-2), 1-12.

Warne, T., Moukhametzianov, R., Baker, J. G., Nehmé, R., Edwards, P. C., Leslie, A. G. W., Schertler, G. F. X., & Tate, C. G. (2011). The structural basis for agonist and partial agonist action on a β1-adrenergic receptor. Nature, 469(7329), 241-244.

Wendell, S. G., Fan, H., & Zhang, C. (2019). G protein–coupled receptors in asthma therapy: Pharmacology and drug action. Pharmacological Reviews, 72(1), 1-49.

Woo, A. Y.-H., Jozwiak, K., Toll, L., Tanga, M. J., Kozocas, J. A., Jimenez, L., Huang, Y., Song, Y., Plazinska, A., Pajak, K., Paul, R. K., Bernier, M., Wainer, I. W., & Xiao, R.-P. (2014). Tyrosine 308 is necessary for ligand-directed gs protein-biased signaling of β2-Adrenoceptor. Journal of Biological Chemistry, 289(28), 19351-19363.

Wu, Y., Zeng, L., & Zhao, S. (2021). Ligands of adrenergic receptors: A structural point of view. Biomolecules, 11(7), 936.

Yamauchi, K., & Ogasawara, M. (2019). The role of histamine in the pathophysiology of asthma and the clinical efficacy of antihistamines in asthma therapy. International Journal of Molecular Sciences, 20(7), 1733.

Zhang, Y., Yang, F., Ling, S., Lv, P., Zhou, Y., Fang, W., Sun, W., Zhang, L., Shi, P., & Tian, C. (2020). Single-particle cryo-EM structural studies of the β2AR–Gs complex bound with a full agonist formoterol. Cell Discovery, 6(1).

Additional Project Information

Project website: -- No project website --
Additional Resources: -- No resources provided --

Research Plan:

Research Plan/Project Summary

Revolutionizing Asthma Treatment: A Breakthrough in LABA Design Enhances Both Selectivity and Efficacy

Student Researcher: Sophia Liu

Adult Sponsor: Dr. Snyder



Despite serving as the frontline treatment of asthma, a disease affecting 262 million patients globally, current long-acting β2-agonists (LABAs) sacrifice efficacy or selectivity for the other, either increasing the risk of cardiac arrest by two-fold so it is no longer usable by patients with cardiovascular diseases or reducing the effects of bronchodilation to the point where it is not suitable for patients with severe asthma (Global Initiative for Asthma, 2022; Lemaitre et al., 2002). The challenge impeding the development of LABAs that are both efficacious and selective is the highly conserved ligand-binding sites shared by the target receptor and the off-target receptor, namely β2-adrenergic receptor (β2AR) and β1-adrenergic receptor (β1AR) (Billington et al., 2016). Indeed, the receptors deviate by merely 0.58 Å. Their gene sequences are 92% similar, differing in only one residue at their orthosteric ligand-binding pocket, making it extremely difficult for a LABA to have a high binding affinity for one and a low binding affinity for the other (Masureel et al., 2018; Woo et al., 2014). 

Recent studies attempt to discover analogs (i.e., drug candidates) of LABA via high-throughput screening of drug databases, but the method is ineffective. This is likely because 2 LABAs and many more within the family of β2-agonists have been approved for use in asthma treatment, and screening doesn't leverage the known interactions engaged by different structural groups of LABA.



This research intends to design the first selective and fully efficacious analog of LABA by proposing and testing a new method of drug design: creating analogs by combining structural groups of different approved LABAs with consideration of known connections between LABAs’ structural groups, receptor interactions, and physiological effects. 


Research Questions:

  • What is the selectivity and efficacy of analogs designed via the proposed method?
  • How does each molecular force acting upon or imposed by current LABA affect the binding affinity, efficacy, and selectivity? 
  • To what degree should structural groups be divided and combined to achieve optimal efficacy and selectivity? 
  • What additional modifications should be made to the analogs to compensate for the slight deviation in binding pose?


If structural groups of different approved LABAs are combined with consideration of of known connections between LABAs’ structural group, receptor interactions, and physiological effects, then the resulting analog will overcome the limitations of current LABAs by having the efficacy of the most efficacious LABA currently and selectivity of the most selective LABA. 


Expected Outcome:

The proposed method will likely result in at least one fully efficacious and selective analog. To demonstrate efficacy, the analog will have an equal to or more negative change in Gibbs free energy (ΔG) than the most efficacious LABA when docked to β2AR and will form all hydrogen bonds necessary to activate β2AR. To demonstrate selectivity, the analog will have an equal to or less negative ΔG than the most selective LABA when docked to β1AR. 



  1. A laptop
  2. Various open-source software including Avogadro (version 1.2) (Hanwell et al., 2012), UCSF Chimera (version 1.17.3) (Petersen et al., 2004), AutoDock Vina (version 2) (Goodsell, 1990), Chemaxon MarvinSketch (version 19) (Chemaxon, 2020), Discovery Studio Visualizer (BIOVIA, 2024) and SwissADME (Daina et al., 2017).
  3. PDB files of salmeterol-bound β2AR (ID: 6MXT), formoterol-bound β1AR (ID: 6IBL), salmeterol (ID: K5Y), formoterol (ID: H98) and vilanterol (ID: GW6)


Protein Preparation

  1. The crystal structure of human β2AR bound to salmeterol and Nb71 (PDB Identification: 6MXT) and the crystal structure of activated turkey β1AR with bound agonist formoterol and nanobody Nb80 (PDB Identification: 6IBL) are imported to the UCSF Chimera from the Protein Data Bank as a *pdb. file. 
  2. For β2AR, residues (H2O, T2O, P33, OLA, and OLC) and chain N are removed to increase computational efficiency. 
  3. For β1AR, residues 2CV, H2O, chains B, C, and D aree removed.
  4. For both receptors, hydrogen atoms and charges are added to the protein to investigate intermolecular forces, and the solvent is removed to reduce computational time. 
  5. The dock-prepped proteins are saved as *mol. and the sessions as *py. files. 

Molecular Docking Experiment of Control Groups

  1. Based on results from x-ray diffraction of human β2AR bound to salmeterol and Nb71, the binding site selected for docking for human β2AR is confined to a box with center at -8.9, -2.48715, 39.0456 and size of 16.4195, 14.641, 19.9925 (Masureel et al., 2018). 
  2. Based on results from x-ray diffraction of activated turkey β1AR with bound agonist formoterol and nanobody Nb80, the binding site selected for docking for β1AR is confined to a box with center -42.7856, 28.2359, -13.9716 and size of 18.8534, 13.031, 21.5095 (Lee et al., 2020).
  3. For each control group (salmeterol, formoterol and vilanterol) the *pdb. file of the LABA is imported to UCSF Chimera and AutoDock Vina.
  4. Each control group is docked to each protein, β2AR, and β1AR, for five trials. The conformation with the most negative ΔG of each trial is selected for further analysis. 

Analog Design and Molecular Docking Experiment of Analogs

  1. Two-dimensional structures of novel analogs of LABA are designed with Chemaxon MarvinSketch and saved as *mol. files. 
  2. The files are then imported to Avogadro and converted into three-dimensional structures. Molecular geometries are optimized based on the universal force field (UFF). 
  3. The analogs are exported as *pdb. files and SMILES files.
  4. The analogs SMILES files are imported into SwissADME to analyze the analogs’ drug-likeness using Lipinski’s rule of five: molecular weight, logP, number of rotatable bonds, number of hydrogen bond acceptors and donors, and surface area. Analogs that do not fulfill the rule are discontinued from the procedure. 
  5. The *pdb. file of each analog is imported to UCSF Chimera and AutoDock Vina.
  6. Each control group is docked to each protein, β2AR, and β1AR, for five trials. The conformation with the most negative ΔG of each trial is selected for further analysis. 

Data Analysis

  1. Seven t-tests are performed to identify any statistically significant differences (1) between individual ligand’s ΔG for β2AR and β1AR, (2-4) between the analog and control groups’ ΔG for β2AR, and (5-7) between analog and control groups’ ΔG for β1AR. 
  2. The binding position with the most negative ΔG among five trials, which is most energetically favorable and thus most likely to happen, is analyzed using Chimera and Discovery Studio Visualizer to identify hydrogen bonds, clashes, and contacts with the default values for relaxing constraints (0.4 Å and 20.0°), (-0.4 Å and 0.0) and (0.4 Å and 0.6) respectively. These, combined with the ΔG, are recorded and analyzed in the context of past literature (Masureel et al., 2018). 
  3. Steps 10-18 are repeated until the analog is shown to be fully efficacious and selective. 

Data Analysis:


Independent Variable: The ligand (analogs or controls) docked to β2AR and β1AR 

Dependent Variable: Selectivity, measured by the ΔG when the ligand is docked to β1AR

Control Variables: 

  • The same computer and the same parameters will be used throughout.
  • The same *pdb. file will be used for the same ligand.
  • The same *pdb. file for the proteins will be used across different ligand experiments.


Independent Variable: The ligand (analogs or controls) docked to β2AR and β1AR 

Dependent Variable: Efficacy, measured by the ΔG and the hydrogen bond network when the ligand is docked to β2AR

Control Variables: 

  • The same computer, the same parameters, and the same *pdb. file for β2AR will be used throughout.

Risk and Safety Evaluation:

No physical danger is associated with conducting this project because all experiments are completed on a computer. To protect myself from internet hazards, I downloaded files from reputable websites. 

Questions and Answers

1. What was the major objective of your project and what was your plan to achieve it? 

The main objective of the project was designing the first selective and fully efficacious analog of long-acting β2-agonist (LABA), which requires overcoming the decades-long challenge posed by the highly conserved ligand-binding sites of β2-adrenoreceptor (β2AR) and β1-adrenoreceptor (β1AR). 


To achieve this goal, I reviewed studies from the past three decades to understand the physiological interactions underlying asthma and the mechanism of action of current LABAs. Observing that studies using conventional methods of virtual high throughput screening of drug databases are ineffective, I proposed a new method: create analogs (i.e., drug candidates) by combining structural groups of different LABAs with consideration of known connections between LABAs’ structural group, receptor interactions, and physiological effects. After mapping out the advantages and disadvantages of each LABA’s structural group based on different studies, I used this knowledge to combine structural groups to design a series of potential analogs. After testing for the analogs’ drug-likeliness, I used molecular docking software to find the intermolecular interactions the analogs engage in with β2AR and β1AR. Then, I analyzed the intermolecular interactions based on previous literature and compared the change in Gibbs free energy (ΔG) of analogs to that of current LABAs (my control groups) as well as the difference in ΔG for when an analog is docked to β2AR and β1AR. Based on these results, I refined the analogs’ chemical structures and repeated the cycle until the objective was met.

A well-thought-out research plan and literature study before designing analogs and conducting computational experiments were essential to organization and success. 


a. Was that goal the result of any specific situation, experience, or problem you encountered?  

Yes, I started to be intrigued by asthma ever since witnessing my friend suffer from an asthma attack after unintentionally walking into a pollen trap. Two years ago, I created an app to help asthma patients travel safely, which became the global winner of a technovation challenge. Since then, I wanted to conduct a study that can have a larger impact on asthma patients. The opportunity came when last year I learned that asthma patients with cardiovascular diseases are prohibited from using a potent asthma treatment known as LABA due to its low selectivity and risk of off-target cardiovascular side effects. Further literature review and discussion with Dr. Snyder revealed the need for a selective LABA without sacrificing efficacy, a research topic I eventually undertook. 


b. Were you trying to solve a problem, answer a question, or test a hypothesis?

All three. Although LABAs have existed for more than half a decade, the highly conserved orthosteric ligand-binding pocket shared by the target receptor β2AR and the off-target receptor β1AR continues to pose a significant barrier for researchers in designing more selective and effective LABAs. Indeed, the receptors deviate by merely 0.58 Å. Their gene sequences are 92% similar, differing in only one residue at their orthosteric ligand-binding pocket (Masureel et al., 2018; Woo et al., 2014). 


Observing that past approaches are ineffective due to failure to leverage known connections between LABAs’ structural group, receptor interactions, and physiological effects, I proposed a new method, which turned out to be successful in overcoming this decades-long challenge.


My proposed solution to the challenge was also a hypothesis. I hypothesize that if structural groups of different approved LABAs are combined, then the resulting analog will have the efficacy of the most efficacious LABA currently and selectivity of the most selective LABA because the resulting analog will retain favorable properties of each structural group and maintain its overall binding position.


Through my study of designing a selective and fully efficacious LABA, I also tried to answer the universal question of how to simultaneously enhance a drug’s binding affinity to its target receptor and decrease its affinity for off-target receptors if both receptors share a highly conserved orthosteric ligand-binding pocket. Given that β2AR and β1AR are one of the most conserved pairs of receptors in humans, the success of this study could help works that attempt to create drugs targeting highly conserved receptors or are in the field of multi-target drugs combining agonists and antagonists. 


2. What were the major tasks you had to perform in order to complete your project?

The project was done independently. The first step was learning how LABAs stimulate β2AR to induce bronchodilation effects via in-depth literature reviews and consulting Dr. Snyder. Despite LABAs emerging in the 1970s, the structures of LABA-bound β2AR and β1AR have not been uncovered until recently. Over the years, numerous researchers have hypothesized the roles of different chemical groups based on experimental results from biochemical assays. Due to the time span during which research have been conducted, I must confirm the validity of past research with recent discoveries. Then, based on my unique idea of combining different LABAs’ structural groups, I used ChemAxon to design the 2d-structure of 76 analogs and SwissADME to select those with drug-like properties. I then used Avogadro to generate the 3D structure of the analogs. Afterward, I retrieved the structures of β2AR and β1AR from the PDB database and prepared them in Chimera. Then, I performed molecular docking on the analogs and control groups using AutoDock Vina. I analyzed the results in Chimera, Discovery Studio Visualizer, and Excel, finding the change in Gibbs free energy (ΔG), performing t-tests, and analyzing the intermolecular interactions to see how the analogs perform in efficacy and selectivity compared to current LABAs. I used insights from my analysis to refine my analogs and repeated the cycle until the objective was met.


3. What is new or novel about your project?

My project is novel in three ways:

  1. The method I used to design my LABA analogs is novel because past research found analogs through the conventional method of high-throughput virtual screening. I created analogs by combining LABA structural groups with consideration of known connections between LABAs’ structural groups, receptor interactions, and physiological effects.
  2. My project designed the first analog of LABA, which was shown to be selective and fully efficacious in silico.
  3. My project designed the first analog that outperformed existing analogs in efficacy and selectivity in silico.


a. Is there some aspect of your project’s objective, or how you achieved it that you haven’t done before?

Yes, even though I had taken chemistry, I was unfamiliar with the standards for small-molecule drugs (for instance, the limit on the number of rotatable bonds and hydrogen donors) and the molecular docking software. However, I quickly acquired the necessary knowledge by reading books suggested by Dr. Snyder and self-learning via online resources.


b. Is your project’s objective, or the way you implemented it, different from anything you have seen?

Past studies have been conducted via virtual high throughput screening, which overlooks recent discoveries that connect LABA’s structural groups to receptor interactions and physiological effects. Moreover, past studies have only succeeded in finding an analog that is either fully efficacious or selective, failing to overcome the decades-long challenge of having both qualities in an analog of LABA. My project is the first to propose creating LABA analogs by combining structural groups of various approved LABAs. My project is also the first to find an analog that is fully efficacious and selective and the first to find an analog that outperforms current LABAs in efficacy and selectivity in silico.


c. If you believe your work to be unique in some way, what research have you done to confirm that it is?

The ZINC database contains information about 750 million commercially available chemical compounds. A search in the database reveals no relevant research on the five analogs I presented, suggesting that the analogs I have designed have yet to be researched. FDA’s database suggests that no new LABA is currently undergoing clinical trials. A thorough search of recent literature also did not reveal the use of my method or any successful attempts at creating selective and fully efficacious analogs of LABA.


4. What was the most challenging part of completing your project?

The most challenging part of the project was analyzing the docking results and getting enough insight to improve my analog. Due to the flexible nature of the analog, any minor changes to the analog intended to increase a hydrogen bond could end up completely changing the analog’s binding position. Moreover, the restrictions on lipophilicity, rotatable bonds, molecular mass, hydrogen bond donors, and acceptors limit many types of modifications on the analog. There is also scarce information in this area specific to LABA, which I can rely on because past research uses virtual high throughput screening and avoids this process. 


a. What problems did you encounter, and how did you overcome them?

The analysis phase presented several hurdles; the first was finding out the contribution of different intermolecular forces to the final Gibbs free energy. Due to the vast interactions involved with LABAs, I had to design a computer program categorizing and distilling the docking results. Second was making improvements to the analog. While all LABAs consist of a head, an ethanolamine, and a tail group, mismatching these structural groups from different LABAs will lead to slight changes in binding poses and, therefore, require slight modifications. Since no literature is specific to this area of LABA, I read texts about the issue in a more general sense: Che202: Organic Chemistry II by Hernan Biava, In silico Drug Design by Kunal Roy, Introduction to Medicinal Chemistry How Drugs Act and Why by Alex Gringauz and Basic Pharmacokinetics and Pharmacodynamics by Sara Rosenbaum. These books gave me insights, and I improved some of my analogs by removing methyl groups, enhancing π interactions by increasing double bonds, substituting with bioisosteres (i.e., chemical groups that produce similar biological effects), increasing structural rigidity by introducing ring structures, increasing hydrophobic interactions by elongating the carbon chain and increasing selectivity by increasing the analog's interactions with the exosite. 


b. What did you learn from overcoming these problems?

One important thing I learned is the need to consult literature throughout the process. Even though formulating a research plan and conducting a literature review seem enough for background study, the books I read during the experimental phase have provided me with many vital insights and helped me solve problems. 


5. If you were going to do this project again, are there any things you would you do differently the next time?

I would be more organized and consistent with my file naming. Due to the vast number of computational trials and analogs that I had to run and design, it became tough to locate files. If I had consistently named the files, it would save a lot of time and effort that I could allocate to my research. Moreover, due to the time span over which LABAs have existed, research varies from being published in recent years to back in the late 1900s. As I conducted the literature review, I found myself using an older article later refuted by results from a newer discovery. If I were to do the project again, I would first find a review article from recent years to understand the fundamentals. Then, I would read about recent discoveries before finally diving into older articles.


6. Did working on this project give you any ideas for other projects? 

Yes. Due to computational power limitations, I could not perform a molecular dynamics simulation alongside molecular docking, which I plan to make up when I can access more powerful computers. I also plan to conduct a biochemical assay to confirm my in silico results when I can access a laboratory.


In addition, as I was conducting my project, I learned that the binding pocket of β2AR is similar to that of the muscarinic receptors, a target receptor of another type of frontline asthma drug known as long-acting muscarinic antagonists (LAMAs). Scientists have improved their understanding of multi-target drugs in recent years, so I hope to design a drug that can simultaneously stimulate β2AR and muscarinic receptors to increase drug adherence, enhance drug efficacy, and reduce medical costs for patients.


7. How did COVID-19 affect the completion of your project?

The project was done via computational docking, so COVID-19 was not a significant issue. However, opportunities to extend the scope of testing the analogs in a wet lab setting were limited due to the pandemic.