Introduction
Modern Pharmaceutical Technologies
The Current Landscape of Obesity Treatment
The Current Landscape of Anxiety and Depression Treatments
The Limitations of Current Pharmacologic Technologies
References
The pharmaceutical industry is charged with creating and developing cures for the many ailments humans contract. For decades, the methodologies and operations used to manufacture these drugs incorporated huge costs, a lack of clinical trials, and a lack of repeatability and reproducibility.
Currently, 21st-century technologies have altered how drug designs are prepared. In silico drug design and in silico clinical trials are being conducted in a manner that exponentially hastens the advance of pharmacotherapy, producing new drug combinations within minutes. Pharmacotherapy, or the treatment of ailments through drugs rather than surgery, radiation, or other modes, is one of the broader fields of biochemistry. Thusly, this article will focus on the pharmacological advancements of obesity, anxiety, and depression.
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Modern Pharmaceutical Technologies
Drugs such as micromolar HIV integrase, cancer drugs, and more are not being tested through traditional means. Instead, they are being fit into virtual models, both animal and human, engineered to mimic the physiology of organs and target moieties. Through blockchain technology, virtual screening, computational biology, and other methodologies, entire drug distribution chains are being refined, leading to a new world of efficacious drugs.
As these methods progress, medical professionals will be able to 3D print personalized drugs in any shape and dosage. All prior methodologies have been employed to aid in creating the obesity and anxiety medication that we will come to discuss.
The Current Landscape of Obesity Treatment
When the general populace discusses obesity management, they often refer to behavioral alterations to lower the number of lipids that the body intakes. However, when an individual progresses towards later stages of obesity, dietary restrictions may not be enough. To address this issue, the modern branch of pharmacotherapy, in conjunction with exploratory AI, has been used to develop and enrich the efficacy of orlistat and its analogs.
First discovered in 1983, orlistat (also known as Xenical) produced by GlaxoSmithKline (GSK) focuses on liquid digestion by blocking pancreatic lipase. This drug effectively decreases the total amount of calories being digested by inhibiting the breakdown of lipids. By limiting the number of smaller base units being produced, we limit the number of calories being absorbed. If we cannot break down lipids, we will not absorb them.
However, this drug does carry with it a slew of side effects, such as constipation, oily stools, incontinence, and other gastrointestinal issues, effects that are to be expected when manually tuning the body's ability to digest. An additional side effect is blocking lipid-soluble vitamins such as vitamins A, D, and K, leading to the additional prescription of vitamin supplements for patients to take orally.
To address these issues, in silico studies are being implemented, using glucosinolates, and plant-derived secondary metabolites, to inhibit pancreatic lipase. Using this machine learning technology, 192 glucosinolate derivatives have been implemented with promising docking scores (a measure of a ligands binding affinity). Technologies used to predict these properties are the QuikProp module of maestro 12.0, MM-GBSA, and GROMACS, used for simulations of ligand complex stability and efficacy.
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The Current Landscape of Anxiety and Depression Treatments
Within the operations of pharmacotherapy in anxiety/depression treatment, three major classes of drugs are considered. These are serotonergic agents, adrenergic agents, and neuroleptics. Serotonergic agents like fluoxetine, or Zoloft, level the amount of serotonin that passes through neuronal synapses.
Adrenergic agents, such as phenylephrine and guanfacine, will target the stress response properties of the brain that are responsible for fight or flight. Finally, neuroleptics, also known as antipsychotic agents, will not specifically target anxiety but control patients' behavioral response.
When treating anxiety and depression through pharmacotherapy means, serotonergic agents and other forms of SSRI (selective serotonin reuptake inhibitors) are predominantly recommended. The serotonin released into synapses will reside there for a longer period. A consequence of circumventing the altering of neurons, and targeting the synapse between such neurons, is fewer side effects and a lesser chance of overdose.
Patients can show significant progress by choosing a suitable SSRI that adapts well to an individual's specified metabolic. The variability in SSRI also speaks volumes about how pharmacokinetics should be conducted. In silico drug designs and AI learning should be implemented to choose a suitable drug for a certain patient. The generational shifts and variability within antidepressants will impact the drug-drug interactions, and greater caution should be exercised before the prescription.
The Limitations of Current Pharmacologic Technologies
Though research remains ongoing, with no signs of haltering or stopping, several questions remain regarding quality control and the automation of drug synthesis. This leads to a broader dilemma within polypharmacy, where western doctors may prescribe a surfeit of different medications, regardless of whether it is appropriate or not. Pharmacotherapy's specific challenges are the long-term safety and usefulness of newer drug models and adult safety profiles.
Computers and in vitro models cannot tell us all we need to know; therefore, appropriate trial design should remain a key focus. Preliminary solutions have been preached by Ian C K Wong, and Tobias Banaschewski, suggesting that an increase in the randomization of placebo-controlled trials may aid in clinical testing. In addition, electronic healthcare records monitoring the long-term safety of medications could save many lives, a practice that not every clinic and hospital adopts.
References:
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- Modanwal, S., Mishra, N. (2022). In Silico Analysis of Glucosinolates as Pancreatic Lipase Inhibitor to Develop Anti-obesity Drug. In: Gunjan, V.K., Zurada, J.M. (eds) Proceedings of the 2nd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. Lecture Notes in Networks and Systems, vol 237. Springer, Singapore. https://doi.org/10.1007/978-981-16-6407-6_37
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- Amekyeh, H., Tarlochan, F., & Billa, N. (2021). Practicality of 3D Printed Personalized Medicines in Therapeutics. Frontiers in pharmacology, 12, 646836. https://doi.org/10.3389/fphar.2021.646836
- Halford JC. Pharmacotherapy for obesity. Appetite. 2006 Jan;46(1):6-10. doi: 10.1016/j.appet.2005.07.010. Epub 2005 Oct 17. PMID: 16229924.
- Strawn, J. R., Geracioti, L., Rajdev, N., Clemenza, K., & Levine, A. (2018). Pharmacotherapy for generalized anxiety disorder in adult and pediatric patients: an evidence-based treatment review. Expert opinion on pharmacotherapy, 19(10), 1057–1070. https://doi.org/10.1080/14656566.2018.1491966
- Ian C K Wong,Tobias Banaschewski,Jan Buitelaar,Samuele Cortese,Manfred Döpfner,Emily Simonoff,David Coghill. (2019) Emerging challenges in pharmacotherapy research on attention-deficit hyperactivity disorder—outcome measures beyond symptom control and clinical trials. The Lancet Psychiatry, Elsevier. DOI: https://doi.org/10.1016/S2215-0366(19)30096-3
Further Reading