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Drug Delivery Systems

Drug Delivery Systems are innovative technologies that enable the targeted and controlled administration of pharmaceutical compounds.
These systems optimize the pharmacokinetics and pharmacodynamics of drugs, improving their therapeutic efficacy and safety profile.
By encapsulating, transporting, and releasing active ingredients, drug delivery systems can enhance bioavailabilty, reduce side effects, and tailor dosage to individual patient needs.
Reasearch in this field leverages cutting-edge materials science, engineering, and computer modeling to develop novel solutions for a wide range of medical applications, from small molecule drugs to biologics and gene therapies.
Discover the latest advancements in drug delivery system design and evaluation using PubCompare.ai, an AI-powered platform that streamlines research and accelerates drug development.

Most cited protocols related to «Drug Delivery Systems»

Molecular Mechanism Analysis of Herbal Formulas

To facilitate the usage, BATMAN-TCM supports users to simply input the formula/herb’s name to analyze its molecular mechanism. This function is based on formula-herb-ingredient association data from TCMID database, which covers 46914 formulas, 8159 herbs and 25210 ingredients6 (link). Known targets of drugs/ingredients were from DrugBank (downloaded on July 26, 2015), KEGG (version: July 31, 2014) and TTD (Therapeutic Target Database) (version: 4.3.02)51 (link). Gene Ontology (GO) functional annotations of human proteins were from NCBI FTP (version: 20111103), human biological pathway data from KEGG database (version: 20140704) and human gene-disease associations parsed from OMIM (Online Mendelian Inheritance in Man) (downloaded on Mar. 13, 2014)52 (link) and TTD (version: 4.3.02). The enrichment analyses of GO functional terms, KEGG biological pathways and OMIM/TTD diseases of a group of protein targets were all based on the hypergeometric cumulative distribution test, and the multiple testing correction was based on Benjamini-Hochberg correction method53 .

Liu Z., Guo F., Wang Y., Li C., Zhang X., Li H., Diao L., Gu J., Wang W., Li D, & He F. (2016). BATMAN-TCM: a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine. Scientific Reports, 6, 21146.

Publication 2016

Biopharmaceuticals Diet, Formula Drug Delivery Systems Gene Annotation Hereditary Diseases Homo sapiens NR4A2 protein, human Staphylococcal Protein A Therapeutics

Comprehensive Traditional Chinese Medicine Database

TCMID is composed of six data fields, namely prescriptions, herbs, ingredients, targets, drugs and diseases. The information and data in those fields were integrated from related web-based databases and text mining of books and published articles.
The prescriptions were collected mainly through text mining from books and published articles. Information for herbs was mainly extracted from TCM-ID database and referred to a book—Encyclopedia of Traditional Chinese Medicines (15 ). The data field about herbal ingredients, such as name and structure, was inputted by combining information from TCM@Taiwan, TCM-ID and Encyclopedia of Traditional Chinese Medicines. Information of diseases and their related proteins, drugs and their targets was retrieved from DrugBank and OMIM. As the target ID used by DrugBank, OMIM and other sources are different from each other, the data from those resources are inconsistent and incomparable. To overcome the barriers, we converted all of them into UniProt AC—a comprehensive, high-quality and freely accessible resource of protein sequence and functional information (16 (link)).
The main goal of our system is to build the connections between the herbal ingredients and diseases through disease genes/proteins, which could also be potential drug targets. To this end, we applied three different methods as follows:
First, we used the information supplied by STITCH (17 (link)), an aggregated database of interactions connecting >300 000 chemicals and 2.6 million proteins. We used the herbal ingredients’ general names and other alternative names to search STITCH and retrieved the related targets (protein); we then converted the corresponding target’s id into UniProt AC for unification purpose.
Second, the information from Herb Ingredients' Targets (HIT), which is extracted from published articles, was collected and integrated into our database.
Finally, as the information from HIT is mainly extracted from articles published in English, while the major TCM researches are in China, and the related research results are mainly published in Chinese, we collected these related articles published in Chinese and manually extracted the related information of ingredients and their targets from them. We used those herb names we collected and one of the following keywords ‘target’, ‘mechanism’, ‘pharmacology’ and ‘pharmacological’ to search Weipu database, which is like PubMed and is a system to host abstracts for the published articles in Chinese. Totally, we manually collected 680 herbal targets from >4500 articles. We also recorded the descriptions for the related experimental evidences and related URL or title for each article.
The six data fields in our database system are connected with their intrinsic relations (Figure 1): a prescription is composed of herbs, a herb contains various ingredients (compounds), an ingredient (or a drug) can interact with its targets (proteins) and a disease could be caused by the dysfunction of genes/proteins.
Figure 1.

Database structure. A–E: six data fields for prescription, herbs, ingredients, diseases, targets and drugs, respectively. 1–5: relationship used to connect each other. 1: prescription is composed of herbs. 2: herb contains ingredients. 3: ingredients can interact with targets. 4: drugs have identified targets. 5: targets may be the causes of disease.

Xue R., Fang Z., Zhang M., Yi Z., Wen C, & Shi T. (2012). TCMID: traditional Chinese medicine integrative database for herb molecular mechanism analysis. Nucleic Acids Research, 41(Database issue), D1089-D1095.

Publication 2012

Amino Acid Sequence Chinese Drug Delivery Systems Gene Products, Protein Pharmaceutical Preparations Proteins

Integrative Protein Network Database

The stringApp retrieves information collected from several source databases. The protein network is imported from the current STRING v10.5^{1 (link)} and augmented with protein− chemical and chemical−chemical associations from the current STITCH version 5.^{7 (link)} This is complemented by drug-target classification from the current release of Pharos^{10 (link)} and information on disease associations, tissue expression, and subcellular localization from the weekly updated databases DISEASES,^{6 (link)} TISSUES,⁹ and COMPARTMENTS.^{8 (link)}Although these databases all provide Application Programming Interfaces (APIs), we mirror the data from the current production versions of STRING and STITCH in a dedicated PostgreSQL database on the same server that already hosts DISEASES, TISSUES, and COMPARTMENTS. This is done both to provide additional functionality over the existing APIs and to allow stringApp to efficiently retrieve all information for a protein network with a single API request.

Doncheva N.T., Morris J.H., Gorodkin J, & Jensen L.J. (2018). Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data. Journal of proteome research, 18(2), 623-632.

Publication 2018

Drug Delivery Systems Proteins Staphylococcal Protein A Tissues

Screening for Cancer Drug Targets

It is a common task to screen for candidate cancer drug targets, oncogenes or suppressor genes from differentially expressed genes based on the comparison of tumors and either matched normal or all normal samples (15 (link),16 (link)). Meanwhile, genes with similar expression patterns that cluster along the chromosome often suggest the underlying genomic mechanisms leading to special expression characteristics (17 (link),18 (link)). Accordingly, GEPIA allows users to input custom statistical methods and thresholds for a given dataset to dynamically obtain differentially expressed genes and their chromosomal distribution (Figure 2B). The statistical methods are presented in supplementary online information (the ‘Help’ page in GEPIA website).

Tang Z., Li C., Kang B., Gao G., Li C, & Zhang Z. (2017). GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Research, 45(Web Server issue), W98-W102.

Publication 2017

Cancer Screening Chromosomes Drug Delivery Systems Gene Expression Genes Genes, Suppressor Genome Neoplasms Oncogenes

Oncogenic Mutation Analysis and Targeted Therapy Matching

To assess clinical actionability of mutations detected by MSK-IMPACT, we annotated sequence mutations, copy number alterations, and rearrangements according to OncoKB, a curated knowledge base of the oncogenic effects and treatment implications of somatic mutations (http://oncokb.org)⁴⁰. Mutations were classified in a tumor type-specific manner according to the level of evidence that the mutation is a predictive biomarker of drug response. Briefly, mutations were classified according to whether they are FDA-recognized biomarkers (Level 1), predict response to standard-of-care therapies (Level 2), or predict response to investigational agents in clinical trials (Level 3). Levels 2 and 3 were subdivided according to whether the evidence exists for the pertinent tumor type (2A, 3A) or a different tumor type (2B, 3B). Tumor samples were annotated according to the highest level of evidence for any mutation identified by MSK-IMPACT.
To determine the rate of enrollment to genomically matched clinical trials, we obtained a list of 850 clinical trials open at MSKCC on which any patient tested by MSK-IMPACT was ever enrolled up to September 2016. After reviewing the enrollment criteria and mechanism of action of each therapy, 197/850 clinical trials were deemed to have a target aberration. A patient was considered to be “matched” if he/she harbored at least one alteration considered to be a target for at least one clinical trial on which they were enrolled. Only patients whose tumors were sequenced during the first 18 months of the MSK-IMPACT sequencing initiative (prior to July 2015) were considered, given that utilization of molecular profiling results and changes to treatment regimens may not occur for many months (or longer) after testing. Of 5,009 patients tested by MSK-IMPACT prior to July 2015, 1,894 (38%) were enrolled on any clinical trial, 811 (16%) were enrolled on a clinical trial with a targeted agent, and 527 (11%) harbored genomic alterations matching the drug target. 72% of all matches occurred after the MSK-IMPACT reports were issued, with the remaining matches based on the results of prior molecular testing.
Clinical responses for patients receiving immunotherapy and targeted BRAF-directed therapy were assessed by detailed chart review. Response was defined as radiographic stable disease or tumor regression at or near 3 months from the initiation of therapy.

Zehir A., Benayed R., Shah R.H., Syed A., Middha S., Kim H.R., Srinivasan P., Gao J., Chakravarty D., Devlin S.M., Hellmann M.D., Barron D.A., Schram A.M., Hameed M., Dogan S., Ross D.S., Hechtman J.F., DeLair D.F., Yao J., Mandelker D.L., Cheng D.T., Chandramohan R., Mohanty A.S., Ptashkin R.N., Jayakumaran G., Prasad M., Syed M.H., Rema A.B., Liu Z.Y., Nafa K., Borsu L., Sadowska J., Casanova J., Bacares R., Kiecka I.J., Razumova A., Son J.B., Stewart L., Baldi T., Mullaney K.A., Al-Ahmadie H., Vakiani E., Abeshouse A.A., Penson A.V., Jonsson P., Camacho N., Chang M.T., Won H.H., Gross B.E., Kundra R., Heins Z.J., Chen H.W., Phillips S., Zhang H., Wang J., Ochoa A., Wills J., Eubank M., Thomas S.B., Gardos S.M., Reales D.N., Galle J., Durany R., Cambria R., Abida W., Cercek A., Feldman D.R., Gounder M.M., Hakimi A.A., Harding J.J., Iyer G., Janjigian Y.Y., Jordan E.J., Kelly C.M., Lowery M.A., Morris L.G., Omuro A.M., Raj N., Razavi P., Shoushtari A.N., Shukla N., Soumerai T.E., Varghese A.M., Yaeger R., Coleman J., Bochner B., Riely G.J., Saltz L.B., Scher H.I., Sabbatini P.J., Robson M.E., Klimstra D.S., Taylor B.S., Baselga J., Schultz N., Hyman D.M., Arcila M.E., Solit D.B., Ladanyi M, & Berger M.F. (2017). Mutational Landscape of Metastatic Cancer Revealed from Prospective Clinical Sequencing of 10,000 Patients. Nature medicine, 23(6), 703-713.

Publication 2017

Biological Markers BRAF protein, human Copy Number Polymorphism Diploid Cell Drug Delivery Systems Drug Kinetics Gene Rearrangement Genome Immunotherapy Mutation Neoplasms Oncogenes Patients Pharmaceutical Preparations Therapeutics Treatment Protocols X-Rays, Diagnostic

Most recents protocols related to «Drug Delivery Systems»

Binding Affinity of Compound 4 to AQP4

Not available on PMC !

Example 3

To support the mechanism of action by which phenylbenzamides directly block AQP4, we perform in vitro binding studies using purified AQP4b and Compound 4 radiolabeled with ³H. Using a Hummel-Dryer style assay, a gel filtration column is equilibratrated with buffer containing detergent, to maintain solubility of AQP4b, and 1 μM [³H]-Compound 4. AQP4b is diluted to 250 μM in this column buffer and incubated at RT for 30 min. The sample is then applied to the column, fractions collected and the presence of [³H]-Compound 4 detected by liquid scintillation counting. FIG. 3 shows the elution profile of [³H]-Compound 4 from the gel filtration column with the elution positions of tetrameric and monomeric AQP4b indicated. The rise in [³H]-Compound 4 from a baseline value of 1 μM represents binding to each of these proteins. Although no monomeric AQP4b can be readily detected in our highly purified AQP4b by conventional means, this assay reveals the presence of a small, albiet vanishing, amount of monomer. The relative affinities for Compound 4 are ˜100 μM and less than 1 μM for tetramer and monomer, respectively. This assay shows relatively weak binding of Compound 4 to solubilized AQP4b; nevertheless, it clearly demonstrates that this phenylbenzamide directly interacts with AQP4b.

US11873266B2. Methods of treating or controlling cytotoxic cerebral edema consequent to an ischemic stroke (2024-01-16). Aeromics, Inc. [US]. Inventors: Marc F. Pelletier [US], George William Farr [US], Paul Robert McGuirk [US], Christopher H Hall [US], Walter F. Boron [US].

Patent 2024

Binding Proteins Biological Assay Buffers Cardiac Arrest Debility Detergents Drug Delivery Systems Drug Kinetics Gel Chromatography Tetrameres

Glycerol-Enhanced Drug Delivery in Conductive Fibers

Not available on PMC !

Example 4

A conductive composite fiber bundle was prepared whose central portion was coated with PDMS in the same manner as Example 4-4. However, the aforementioned conductive composite fiber bundle was impregnated with glycerol before being coated with the PDMS. Upon measuring drug delivery speed in the same manner as Example 4-4 using this conductive composite fiber bundle, the concentration of the Lucifer yellow in the dish increased at a rate of 6.7 μM/day (in FIG. 36, the ▪ plot and the solid line). From this result, it is shown that drug delivery speed is increased by adding glycerol to the conductive composite fibers.

As one reason for the improvement in drug delivery speed due to impregnation of conductive composite fibers with glycerol, it would seem that when the conductive composite fibers are coated with PDMS, the glycerol prevents the PDMS from penetrating (infiltrating) to the interior of the conductive composite fiber bundle, and the condition of the flow path constituted by the conductive composite fiber bundle is maintained in a condition suited to drug transport.

US11862359B2. Conductive polymer fibers, method and device for producing conductive polymer fibers, biological electrode, device for measuring biological signals, implantable electrode, and device for measuring biological signals (2024-01-02). NIPPON TELEGRAPH AND TELEPHONE CORPORATION [JP]. Inventors: Shingo Tsukada [JP], Hiroshi Nakashima [JP], Akiyoshi Shimada [JP], Koji Sumitomo [JP], Keiichi Torimitsu [JP].

Patent 2024

Drug Delivery Systems Electric Conductivity Fertilization Fibrosis Glycerin Hyperostosis, Diffuse Idiopathic Skeletal lucifer yellow Pharmaceutical Preparations

Superolateral Knee Injection for Drug Delivery

Not available on PMC !

Example 6

A plain radiography of patient X's knee is undertaken to evaluate the path of least obstruction and maximal access to the synovial cavity. This access can be superolateral, supermedial or anteromedial/anterolateral. The knee injection site is selected based on the bony anatomy of the patient X's knee joint. In the case of patient X, a superolateral knee injection site is chosen.

Patient X lies supine with the knee fully extended with a thin pad support to facilitate relaxation. The injection site is marked with a pen to leave an impression on the skin and the skin is cleaned with alcohol swabs.

A clinician's thumb is used to gently stabilize the patella while a 25 G 1.5″ needle containing the degradable drug delivery composition with celecobix is inserted underneath the supralateral surface of the patella aimed toward the center of the patella and then directed slightly posteriorly and inferomedially into the knee joint. The content of the needle is then injected and the needle is withdrawn from the knee.

US11865205B2. Method for morselizing and/or targeting pharmaceutically active principles to synovial tissue (2024-01-09). MEDINCELL [FR]. Inventors: Georges Gaudriault [FR], Sylvestre Grizot [FR], Mark Hurtig [CA], Matthew Shive [US].

Patent 2024

Bones Dental Caries Drug Delivery Systems Ethanol Intra-Articular Injections Joints Knee, Fractured Knee Joint Needles Patella Patients Skin Thumb X-Rays, Diagnostic

Self-Microemulsifying Ticagrelor Delivery System

Not available on PMC !

EXAMPLE 11

A self-microemulsifying formulation (total weight: 800 mg) modified from (Na et al. Strategic approach to developing a self-microemulsifying drug delivery system to enhance antiplatelet activity and bioavailability of ticagrelor. Int J Nanomedicine. 2019 Feb. 15; 14:1193-1212; CN104971042A; and KR102007731B1 (each of which is incorporated herein by reference in its entirety)), was prepared by mixing, an oil (Capmul MCM; 45.0 w/w %), surfactant agent (Cremophor EL; 38 w/w %) and a co-surfactant (Transcutol P; 17 w/w %). The mixture was gently stirred to make a uniform solution, then, 100 mg ticagrelor and 100 mg zinc caprylate were added and mixed to make a uniform formulation. Each drop of the mixture contains 5 mg of ticagrelor and 5 mg of zinc caprylate.

IngredientsAmount (mg)

Ticagrelor (mg)100

Zinc caprylate (mg)100

Capmul MCM (mg)360

Cremophor EL (mg)304

Transcutol P(mg)136

US11878016B2. Methods and products for treating subjects with autism spectrum disorders (2024-01-23). NEURIM PHARMACEUTICALS (1991) LTD. [IL]. Inventors: Nava Zisapel [IL], Moshe Laudon [IL].

Patent 2024

Autism Spectrum Disorders Capmul MCM caprylic acid, zinc salt cremophor EL Drug Delivery Systems Surfactants Ticagrelor transcutol P

Fumagillin Nanoparticle Therapy for Angiogenesis

Not available on PMC !

Example 13

As shown in FIG. 19, fumagillin dissolved in a lipid membrane rapidly releases in vivo, making it practically ineffective in vivo. FIG. 20 shows, however, the in vivo effectiveness of a fumagillin prodrug administered in a nanoparticle of the invention. In particular, the figure shows the in vivo MR signal enhancement post treatment with targeted fumagillin nanoparticles (a-b) and control (no drug, c-d); Reduced Matrigel implant volume (%) in rats treated with αvβ3-integrin-targeted nanoparticles with 2.28 mole % fumagillin-PD vs. αvβ3-integrin-targeted nanoparticles with 2.28% fumagillin, αvβ3-integrin-targeted nanoparticles without drug, nontargeted nanoparticles with 2.28 mole % fumagillin-PD.

FIG. 21 shows the effect of the fumagillin prodrug in an in vitro cell proliferation assay. The left panel shows the effects of fumagillin prodrug incorporated nanoparticles and control nanoparticles (targeted no drug, non targeted and targeted fumagillin) on human umbilical vein endothelial cells (HUVEC) for cell proliferation by CyQuant NF assay and the right panel shows cell metabolic activity by Alamar Blue assay.

US11872313B2. Prodrug compositions, prodrug nanoparticles, and methods of use thereof (2024-01-16). Washington University [US]. Inventors: Gregory M. Lanza [US], Dipanjan Pan [US].

Patent 2024

Alamar Blue Biological Assay Cell Proliferation Cells Drug Delivery Systems fumagillin Human Umbilical Vein Endothelial Cells Integrin alphaVbeta3 matrigel Membrane Lipids Nevus Patient Discharge Pharmaceutical Preparations Prodrugs Rattus

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DMSO is a versatile organic solvent commonly used in laboratory settings. It has a high boiling point, low viscosity, and the ability to dissolve a wide range of polar and non-polar compounds. DMSO's core function is as a solvent, allowing for the effective dissolution and handling of various chemical substances during research and experimentation.

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Fetal bovine serum (fbs) by Thermo Fisher Scientific

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Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.

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Bovine serum albumin (BSA) is a common laboratory reagent derived from bovine blood plasma. It is a protein that serves as a stabilizer and blocking agent in various biochemical and immunological applications. BSA is widely used to maintain the activity and solubility of enzymes, proteins, and other biomolecules in experimental settings.

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What are the key benefits of using drug delivery systems?

Drug delivery systems offer several key benefits over traditional pharmaceutical administration methods. They can enhance the bioavailability of drugs, reduce side effects, and allow for more targeted and controlled release of active ingredients. By optimizing the pharmacokinetics and pharmacodynamics of drugs, delivery systems can improve therapeutic efficacy and safety profiles. These innovative technologies leverage cutting-edge materials science, engineering, and computer modeling to develop solutions for a wide range of medical applications, from small molecule drugs to biologics and gene therapies.

How can PubCompare.ai assist in researching drug delivery systems?

PubCompare.ai is an AI-powered platform that can streamline research and accelerate drug development for drug delivery systems. The platform allows you to screen protocol literature more efficiently, and leverages AI to pinpoint critical insights. PubCompare.ai can help researchers identify the most effective protocols related to drug delivery systems for their specific research goals. The platform's AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accuracy.

What are the different types or variations of drug delivery systems?

Drug delivery systems come in a variety of forms, including nanoparticles, liposomes, microparticles, implants, transdermal patches, and more. Each type is designed to address specific challenges, such as improving solubility, prolonging drug release, or targeting specific tissues or cells. The choice of delivery system depends on the physicochemical properties of the drug, the desired pharmacokinetic profile, and the intended route of administration, such as oral, parenteral, or topical.

What are some common medical applications of drug delivery systems?

Drug delivery systems have a wide range of medical applications, from treating cancer and infectious diseases to managing chronic conditions and delivering gene therapies. For example, nanoparticle-based systems can be used to target tumors and deliver chemotherapeutic agents more effectively. Implantable drug delivery devices can provide sustained release of medications for pain management or hormonal therapies. Transdermal patches can be used to deliver drugs through the skin, avoiding first-pass metabolism. The versatility of drug delivery systems allows for customized solutions to meet the unique needs of different patient populations and medical conditions.

How can users address common challenges with drug delivery systems?

One common challenge with drug delivery systems is ensuring the stability and integrity of the formulation during storage and administration. Proper handling and storage conditions are crucial to maintain the desired release profile and therapeutic efficacy. Another challenge is overcoming biological barriers, such as the blood-brain barrier or the intestinal epithelium, to effectively deliver drugs to the target site. Careful design of the delivery system, including the selection of materials and targeting ligands, can help address these challenges. Researchers can also leverage PubCopmparr.ai to identify the most promising protocols and solutions for their specific drug delivery needs.

More about "Drug Delivery Systems"

Innovative Drug Delivery Technologies: Revolutionizing Pharmaceutical Administration Drug delivery systems (DDS) are cutting-edge technologies that enable the targeted and controlled administration of pharmaceutical compounds.
These advanced systems optimize the pharmacokinetics and pharmacodynamics of drugs, enhancing their therapeutic efficacy and safety profile.
By encapsulating, transporting, and precisely releasing active ingredients, DDS can improve bioavailability, reduce side effects, and tailor dosage to individual patient needs.
The field of drug delivery leverages the latest advancements in materials science, engineering, and computer modeling to develop novel solutions for a wide range of medical applications, from small molecule drugs to biologics and gene therapies.
Researchers explore a diverse array of delivery methods, including nanoparticles, liposomes, hydrogels, and transdermal patches, to overcome challenges such as poor solubility, low permeability, and targeted delivery.
Emerging techniques like DMSO (dimethyl sulfoxide) and PHM-100 (a polymeric micelle formulation) demonstrate the potential to enhance drug absorption and distribution.
Computational tools like AutoDock Tools and FBS (fetal bovine serum) simulations aid in the design and evaluation of drug delivery systems, while ENV-008CT and MED-PC IV software enable the analysis of in vitro and in vivo performance.
Cutting-edge bioassays, such as the CellTiter-Glo luminescent cell viability assay and Bovine serum albumin (BSA) protein quantification, provide valuable insights into the safety and efficacy of drug delivery systems.
Additionally, the use of lidocaine, a local anesthetic, highlights the diverse applications of these innovative technologies, which can improve patient comfort and compliance.
Explore the latest advancements in drug delivery system design and evaluation using PubCompare.ai, an AI-powered platform that streamlines research and accelerates drug development.
Discover the power of this innovative tool to revolutionize your pharmaceutical research and development efforts.