Advances in Bioanalytical Methods for Modern Drug Development

Introduction

Importance of Bioanalysis

Bioanalysis is used to measure biological molecules and xenobiotics in the living system. It assists in outlining the absorption, distribution, metabolism, and excretion of drugs. Proteins, peptides, nucleic acids, sugars, amino acids, hormones, lipids, vitamins, and other natural compounds are all biological molecules. Most of them are biomarkers and assist in comprehending disease and drug action. Xenobiotics are the substances that enter the organism in particular in the form of drugs, metabolites, impurities, and toxins, which are not inherently present there. Their measurement is used to measure drug safety and pharmacokinetics. Bioanalysis gives PK profiles, PD correlations, bioequivalence data and biomarker levels whereas pharmaceutical analysis verifies the identity, purity, potency, and stability of drug substances and products (Figure 1). This is a critical process in every phase of drug discovery up to post-marketing¹.

Figure 1: Union and distinction of bioanalysis and pharmaceutical analysis Click here to View Figure

Early techniques involved colorimetric and spectrophotometric techniques (Figure 2). Separation was enhanced using HPLC and GC. Sensitivity was enhanced in GC-MS and LC-MS. Recent methods now involve ligand-binding assays, immunoaffinity LC-MS, and omics-based methods². Modern bioanalysis is used to support small molecules, biosimilars, biomarkers, gene and cell therapies, point-of-care testing and biosensor-based detection. It is also useful in toxicology, diagnosis, and real- time monitoring³. There are strict guidelines followed in bioanalytical methods in FDA, EMA, and ICH (M10). The guidelines of FDA are concerned with accuracy, precision, selectivity, sensitivity, and stability. EMA guidelines consider cross-validation, method transfer and matrix effects. The ICH M10 guideline balances the two approaches^4-6.

Figure 2: Historical evolution of bioanalytical techniques Click here to View table

Classical Analytical Techniques: A Foundation

Bioanalytical analysis relies on traditional methods like UV-visible spectroscopy, infrared spectroscopy, fluorescence spectroscopy, and fundamental chromatographic techniques like TLC and column chromatography for everyday analysis and method development. Traditional analytical methods (as summarized in Table 1) in bioanalytical analysis are still prevalent, particularly in small labs.

Table 1: Comparison of spectroscopic techniques in bioanalytical applications

Chromatographic Techniques

Chromatography helps in the identification, separation, and quantification of the bioanalysis since it forms a very important part of the bioanalysis process. It is necessary in regulatory filings, pharmacokinetic studies, and impurity profiling (Table 2). Bioanalytical analysis requires use of contemporary chromatographic methods such as HPLC and UHPLC. They are better in resolution, speed and eco-compatibility.

Table 2: Chromatographic techniques in bioanalysis

 Mass Spectrometry and Coupled Techniques

Mass spectrometry (MS) is a crucial tool in bioanalytical research. It enables identification, characterization, and quantification of metabolites, and biomolecules. Its combination with chromatographic systems, tandem MS, and high-resolution MS has transformed bioanalysis (Table 3). Mass spectrometry has revolutionized the pharmaceutical industry by enabling high-throughput, ultrasensitive analysis of medicines, metabolites, and biomolecules. Its applications in systems biology, metabolomics, and proteomics have enabled precision medicine and customized treatments. Proficiency in ionization methods is crucial¹⁷.

Table 3: Mass spectrometry and coupled techniques in pharmaceutical bioanalysis

Electrochemical and Biosensor-Based Techniques

Potentiometry, amperometry, and voltammetry are important electrochemical methods in bioanalysis (Table 4). Potentiometry measures ion activity using ion-selective electrodes. It is used for electrolyte testing, heparin/protamine detection, and immunoassay biosensors. Amperometry measures current at a fixed potential. It is common in glucose biosensors, flow-injection drug analysis, and vesicle release studies. Voltammetry detects analytes using varying potentials. It is useful for trace metals and drug quantification in biological samples. These methods are sensitive, selective, low-cost, and easy to miniaturize. They are suitable for point-of-care and rapid testing. Bioanalysis is moving toward electrochemical and biosensor-based platforms. Electrophoretic techniques also help to analyze complex biological samples.

Capillary electrophoresis (CE) is a fast and efficient separation technique used in bioanalysis. It needs very small sample volumes and separates analytes based on their electrophoretic mobility in a narrow capillary under an electric field³⁷. CE can achieve very high separation efficiency, up to 600,000 theoretical plates per meter. It is used for DNA sequencing, genotyping, protein and peptide analysis, and measuring small drugs and metabolites in biological samples. When combined with MS or fluorescence detection, it gives higher sensitivity for biomarkers, forensic testing, and drug monitoring. CE uses little solvent and works well with complex samples, making it useful for high-throughput testing and metabolomics^38-40.

Some methods of luminescence, which are employed in trace analysis, sensitivity, impurity detection, and monitoring of low concentrations, consist of fluorimetry and phosphorimetry. Fluorimetry is a process whereby molecules absorb light and re-emit it instantly or slowly. These methods are very sensitive particularly in cases where the background absorption is very low. They are applicable in drug testing, impurity testing, monitoring of drug release, drug-protein and drug-ligand interactions, and stability testing.  Fluorimetry is particularly useful for identifying low-level contaminants and determining impurities in pharmaceutical formulations. On the other hand, phosphorimetry is employed when extended emission durations are useful, such as in low temperature or solid phases, or to distinguish between background fluorescence and scatter. It has low detection limits and straightforward sample handling. However, these techniques have drawbacks, such as internal filter effects, overlap in excitation/emission spectra, photobleaching, quenching, and limited applicability. Reproducibility may be compromised in some situations due to background light interference or stray fluorescence^41-43.

Titration or volumetric analysis, is a well-established method of quantitation in pharmaceutical analysis. It entails the reaction of the definite quantity of the chemical reagent to analyte sample in such a way so that the reactants attain a certain point of equivalency. They include acid-base neutralization, redox titrations, precipitation titrations, complexometric titrations as well as non-aqueous titrations. These techniques are also usually employed in pharmacopeias to control the quality of raw materials, dosage forms, and bulk medications. Nevertheless, titrimetric methods have some shortcomings, including the use of pure analyte, clarity of endpoints and stochiometric reactions. Visual endpoints can be obscured due to impurities, interfering species or colored matrices. The medication that has buffering strength or weak acids/bases might need non-aqueous titration. Titrimetry might not be a good method on trace contaminants or low concentration analysis because of their low level of sensitivity. These limitations are reducing with new methods of titration, including potentiometric endpoints, non-aqueous titrations, and micro-titrations^44-45.

Table 4: Electrochemical and biosensor techniques in bioanalysis

 Spectroscopic Techniques Beyond Basics

Surface-enhanced Raman spectroscopy (SERS) is a highly sensitive bioanalytical technique. It uses gold or silver nanoparticles to greatly amplify Raman signals. This enhancement can reach 10*10–10*11, allowing the detection of single molecules in biological samples. SERS gives fast and nondestructive molecular information. It works well in aqueous samples and needs very little sample preparation.

SERS is widely used in bioanalysis (Table 5). It helps detect pathogens at the single-cell level, including antibiotic-resistant bacteria such as S. aureus and P. aeruginosa directly from patient samples. It supports early cancer diagnosis and can achieve high prediction accuracy. SERS is also useful for DNA and nucleic acid sensing in disease detection. It is applied in therapeutic drug monitoring, live-cell imaging, metabolic profiling, and trace biomarker detection in serum and plasma.

Table 5: Comparative summary of advanced spectroscopic techniques in bioanalysis

 Ligand Binding Assays and Immunoassays

LBAs (Ligand binding assays) and immunoassays are crucial bioanalytical tools for measuring big biomolecules like proteins, peptides, antibodies, and hormones. They offer excellent specificity and sensitivity, crucial for clinical diagnostics, medication monitoring, biomarker identification, and biologics development (Table 6). Ligand binding assays and Immunoassays are crucial for biotherapeutics and biomarkers detection and quantification. They have evolved from ELISA and RIA to multiplexed platforms like Luminex and MSD, becoming essential for clinical trials and regulatory compliance.

Table 6: Summary of ligand binding assays and immunoassay platforms

 Emerging and Hybrid Techniques in Bioanalysis

Advanced bioanalytical methods that need minimal sample preparation and offer real-time, high-resolution measurements are gradually replacing traditional LC-MS/MS and immunoassays. Imaging mass spectrometry maps the spatial distribution of drugs and metabolites directly from tissue surfaces through processes such as MALDI desorption–ionization. Ambient ionization MS techniques, like DESI and DART, allow direct ionization of samples without extraction or chromatography. Microfluidics-based lab-on-a-chip platforms perform separation, reaction, and detection in miniaturized channels using very small sample volumes. AI and ML algorithms process large spectral datasets, remove noise, detect patterns, and predict pharmacokinetic or toxicity outcomes. These mechanisms enable rapid, sensitive, and spatially resolved characterization of analytes. Together, these innovations strengthen bioanalytical workflows, improve understanding of drug behavior, and support personalized medicine.

Ambient ionization mass spectrometry (AI-MS) One technique which enables the analysis of a sample in real-time and in the natural state at atmospheric pressure is ambient ionization mass spectrometry (AI-MS). Its major characteristics are speed of analysis, little sample preparation, compatibility with complicated matrices and high-resolution MS systems.

Desorption Electrospray Ionization (DESI) The technique is called Desorption Electrospray Ionization (DESI), and it is a technique that integrates electrospray ionization and desorption, enabling the analysis of a sample with a high level of spatial resolution, operates in the ambient and sensitive drug profiling. They are used in forensic pharmacological analysis, localization of drugs and deterioration products on packaging surfaces.

In the Direct Analysis in Real Time (DART) technique, the ionizing analytes is treated with hot gas stream. It is fast, needs little preparation, and can be used in the metabolite screening, drug counterfeiting detection, and in API quality monitoring.

Imaging mass spectrometry (IMS): Imaging mass spectrometry (IMS) is a combination of imaging and analytical techniques that visualizes the spatial distribution of molecules, such as proteins, metabolites or drugs, directly at the surface of a sample, e.g., a tissue section, without requiring separation or purification steps.

Matrix-Assisted Laser Desorption/Ionization (MALDI): Matrix-Assisted Laser Desorption/Ionization (MALDI) Imaging is an extensive method that works well with proteins, peptides and lipids. It provides high-resolution imaging, often with time-of-flight analyzers, and finds use in lipidomics, proteomics, tissue distribution analysis and tumor mapping.

Elemental and small molecule analysis, such as metabolite monitoring, pharmacodynamics studies, and preclinical toxicity screening is done using secondary ion mass spectrometry (SIMS) and DESI imaging. DESI imaging is a particular combination of the ambient ionization and spatial mapping.

Lab-on-a-Chip (LOC) systems are analytical tools that incorporate detection, separation, reaction and sample preparation on a microchip. Such systems are characterized by high throughput, low use of reagents and automation compatibility.

Droplet-Based microfluidics systems In Droplet-Based microfluidics systems, sample processing and analysis are performed by small droplets of immiscible carriers (e.g., oil). This technology assists in different assays such as PCR, ELISA, MS, Fluorescence, and digital bioassays, which allow the resolution of single-cell or single-molecule. The method allows the label-free quantification of analytes and the real-time reaction monitoring by combining with fluorescence, Raman, and electrochemical detection systems. It is designed to be used in high throughput applications such as pharmacogenetic profiling, therapeutic drug monitoring, and portable diagnostics during clinical trials.

Artificial Intelligence/Machine Learning (AI/ML) to preprocess data, extract features, remove baselines and normalize, smooth, reduce dimensions, and pick peaks. CNNs and decision trees are better than traditional algorithms, whereas random forest models are utilized to recognize the peaks in noisy MS datasets. Quality control, predictive modeling, pattern identification, outlier detection, and concentration prediction also are control tasks carried out with AI. The AI-based health application has proven to be effective in tumor margin identification, metabolic flux analysis, space pharmacokinetic model, and real-time forensic pharmaceutical analysis. Some of the central issues include standardization and validation of AI models, sample variability, cost and fabrication barriers, and data overload in imaging mass spectrometry. The future directions include the integration of multi-omics, AI-driven automation of the laboratory, and sensors as a part of bioanalytical systems^60-64.

Comparison of conventional and modern Methods^65-66

 Case Studies Highlighting Advances in Bioanalysis

Case study 1: According to the single cell proteomics analysis of drug response, Straubhaar et. al. has demonstrated that it can be used as a drug discovery platform.

Rationale

Single-cell analysis in biology and biomedicine has been an important method of cell association, which entails cell type, states, transitions and localization. Such methods as the use of fluorescence tags, nanoscale super-resolution imaging, single-cell RNA sequencing, and proteomics shed more light on biological processes that cannot be depicted by bulk analysis. The discipline makes possible studying variability in drug response and advancing the understanding of complex systems, including cancer, inflammation and aging. The current developments in the field of single cell proteomics enable the measurement of thousands of proteins in individual cells. The article presents a new mass spectrometry-based single-cell proteome analysis, SCREEN, that is expected to be much more efficient and quicker than the old ones. The article examines the proteome profiling in varying drug conditions to examine the heterogeneity of cancer cells and deals with issues and current trends in single-cell proteomics⁶⁷.

Case study 2: Blas-Munoz et. al. reveals a variety of keto reductases important in pharmaceutical production in case study 2 through the use of a microfluidics-based ultra-high-throughput screening.

Rationale

Keto reductases (KREDs) are valuable biocatalysts to the synthesis of enantioselectively chiral alcohols. Here, Thai et al. optimized the low-throughput assay to a ultrahigh-throughput screening platform, that relies on droplet microfluidics, fluorescence-activated cell sorting (FACS) and fluorescence-based detection. This new technique has been effective to screen 1.5 million clones and has been successful in identifying KREDs that are less homologous to known enzymes and have novel substrate specificities. It also made the initial screening of KRED in microdroplets by metagenomic screening a reality, and opened up the prospects of large-scale enzyme discovery and evolution screenings⁶⁸.

Case study 3: Optimizing drug discovery: Surface plasmon resonance techniques and their multifunctional uses by Acharya et. al.

Rationale

Surface Plasmon Resonance (SPR) sensors have been developed, the advantages include real-time tracking of biomolecular interactions, label-free detection, high sensitivity, and ability to analyze multiple layers at the same time. These properties make SPR biosensors very important in the pharmaceutical industry to ascertain the efficacy and safety of therapeutic effects. The review identifies the current developments in SPR biosensors, their use in drug development, challenges and prospects in the pharmaceutical sector⁶⁹.

Bioanalytical Sample Analysis Workflow

A bioanalytical workflow in sample-analysis is described by the Figure 3. It begins with the definition of the goal of the analysis and the choice of the most appropriate method to the target molecule. It is then followed by sample collection and preparation to represent clean and uniform samples. The method conditions, instrument setups are optimized and calibration standards and quality controls are run. The samples are then subjected to analysis and quantitative and interpretative data generated are processed. Validation tests are used to ensure that the method is accurate, precise, and reliable. The final stage of the work is the preparation of a finished report, which records the findings, chromatograms, spectra, and conclusions^70-72.

Figure 3: Bioanalytical sample analysis workflow Click here to View Figure

Conclusion

To summarize, new bioanalytical technologies are greatly improving pharmaceutical research. Classical methods such as spectroscopy, chromatography, mass spectrometry, electrochemistry, biosensors, ligand-binding assays, and immunoassays still provide reliable results, but they often need more sample preparation and take longer to analyze. Modern tools such as ambient ionization MS, imaging MS, microfluidic lab-on-a-chip systems, and AI/ML analytics offer faster testing, less sample handling, higher sensitivity, and spatial or real-time information. Their main limitations include high instrument cost, complex data processing, and limited regulatory acceptance.

Future work should focus on making these methods easier to use, more standardized, and more compatible with routine laboratory workflows. Stronger automation and clear regulatory guidelines will support wider implementation. As these technologies grow, they will speed up drug discovery, improve pharmacokinetic and biomarker studies, and advance personalized medicine.

Acknowledgment

The authors sincerely acknowledge their respective universities for their continuous support throughout this work.

Funding Sources

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Conflict of Interest

The author(s) do not have any conflict of interest.

Data Availability Statement

This statement does not apply to this article.

Ethics Statement

This research did not involve human participants, animal subjects, or any material that requires ethical approval.

References

1. Viswanathan CT.; Bansal S.; Booth B.; DeStefano AJ.; Rose MJ.; Sailstad J.; Shah VP.; Skelly JP.; Swann PG.; Weiner R. Quantitative bioanalytical methods validation and implementation: Best practices for chromatographic and ligand binding assays., AAPS J., 2007, 9(1), E30–E42. doi: https://doi.org/10.1208/aapsj0901004.
   CrossRef
2. Jemal M. High-throughput quantitative bioanalysis by LC/MS/MS., Biomed. Chromatogr., 2000, 14(6), 422–429. doi: https://doi.org/10.1002/1099-0801(200010)14:6.
   CrossRef
3. Smolec J.; DeSilva B.; Smith W.; Weiner R.; Kelly M.; Lee B.;  Khan M.; Tacey R.; Hill H.; Celniker A.; Shah V.; Bowsher R.; Mire-Sluis A.; Findlay JWA.; Saltarelli M.; Quarmby V.; Lansky D.; Dillard R.; Ullmann M.; Keller S.; Karnes HT. Bioanalytical method validation for macromolecules in support of pharmacokinetic studies., Pharm Res., 2005, 22(9), 1425–1431. doi: https://doi.org/10.1007/s11095-005-5917-9.
   CrossRef
4. U.S. Food and Drug Administration. Bioanalytical Method Validation: Guidance for Industry. Silver Spring, MD: U.S. FDA; 2018.
5. European Medicines Agency. Guideline on Bioanalytical Method Validation. EMA/CHMP/EWP/192217/2009; London, UK: EMA; 2011.
6. International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. ICH Harmonised Guideline: Bioanalytical Method Validation and Study Sample Analysis (M10). ICH; 2022.
7. Beckett AH, Stenlake JB. Practical Pharmaceutical Chemistry. Vol. 1, 4th ed.; New Delhi: CBS Publishers & Distributors; 2006:275–300. ISBN: 978-9387964495.
8. Silverstein RM, Webster FX, Kiemle DJ, Bryce DL. Spectrometric Identification of Organic Compounds. 8th ed.; Hoboken, NJ: Wiley; 2014:71-125. ISBN: 978-0-470-61637-6.
9. Lakowicz JR. Principles of Fluorescence Spectroscopy. 3rd ed.; New York: Springer; 2006:1-26. Doi: https://doi.org/10.1007/978-0-387-46312-4
   CrossRef
10. Stahl E. Thin Layer Chromatography: A Laboratory Handbook. 2nd ed.; Berlin: Springer; 1969;1-400. Doi: https://doi.org/10.1007/978-3-642-88488-7
    CrossRef
11. Poole CF. The Essence of Chromatography. 2nd ed.; Amsterdam: Elsevier; 2013:1-78. ISBN: 0444501983.
12. Dong, M. W. Modern HPLC for Practicing Scientists; John Wiley & Sons: 2006:1-100. ISBN 978-0-471-72789-7.
13. Grob RL, Barry EF. Modern Practice of Gas Chromatography. 4th ed.; Hoboken, NJ: Wiley; 2004:1-100, 739-768. Doi: https://doi.org/10.1002/0471651141.
    CrossRef
14. Swartz M. Ultra performance liquid chromatography (UPLC): an introduction and review., J Liq Chromatogr Relat Technol., 2005, 28(7–8), 1253–1263. doi: https://doi.org/10.1081/JLC-200053046.
    CrossRef
15. Webster GK. Supercritical Fluid Chromatography: Advances and Applications in Pharmaceutical Analysis. Singapore: Pan Stanford Publishing; 2014:1-50. Doi: https://doi.org/10.4032/9789814463010
16. Berger TA. Instrumentation for analytical scale supercritical fluid chromatography., J Chromatogr A., 2015, 1421, 171–183. doi: https://doi.org/10.1016/j.chroma.2015.07.062.
    CrossRef
17. Gross JH. Mass Spectrometry: A Textbook. 3rd ed.; Cham, Switzerland: Springer; 2017:1-50. Doi: https://doi.org/10.1007/978-3-319-54398-7.
    CrossRef
18. Sparkman OD, Penton Z, Kitson FG. Gas Chromatography and Mass Spectrometry: A Practical Guide. 2nd ed.; Oxford, UK: Academic Press; 2011:1-78. Doi: https://doi.org/10.1016/C2009-0-17039-3.
    CrossRef
19. Jajoo VS.; Sawale AV. Recent advances in supercritical fluid chromatography., Res J Sci Technol., 2024, 16(1), 87–96. doi: https://doi.org/10.52711/2349-2988.2024.00014.
    CrossRef
20. Allen DR.; McWhinney BC. Quadrupole time-of-flight mass spectrometry: A paradigm shift in toxicology screening applications.. Clin Biochem Rev., 2019, 40(3), 135–146. doi: https://doi.org/10.33176/AACB-19-00023.
    CrossRef
21. Lin C.; Tian Q.; Guo S.; Xie D.; Cai Y.; Wang Z.; Chu H.; Qiu S.; Tang S.; Zhang A.  Metabolomics for clinical biomarker discovery and therapeutic target identification., Molecules., 2024, 29, 2198. doi: https://doi.org/10.3390/molecules29102198.
    CrossRef
22. Timerbaev AR.; Pawlak K.; Aleksenko SS.; Foteeva LS.; Matczuk M.; Jarosz M. Advances of CE-ICP-MS in speciation analysis related to metalloproteomics of anticancer drugs., Talanta., 2012, 102, 164–170. doi: https://doi.org/10.1016/j.talanta.2012.07.031.
    CrossRef
23. Matczuk M.; Ruzik L.; Timerbaev AR. Recent development of CE-ICP-MS in biospeciation research and analysis: From anticancer drugs to nanoparticles and beyond., TrAC Trends Anal Chem., 2024, 180, 117967. doi: https://doi.org/10.1016/j.trac.2024.117967.
    CrossRef
24. Rao RN.; Talluri MVNK. An overview of recent applications of inductively coupled plasma–mass spectrometry (ICP-MS) in determination of inorganic impurities in drugs and pharmaceuticals., J Pharm Biomed Anal., 2007, 43(1), 1–13. doi: https://doi.org/10.1016/j.jpba.2006.07.004.
    CrossRef
25. Mischak H.; Schanstra JP. CE-MS in biomarker discovery, validation, and clinical application., Proteomics Clin Appl., 2011, 5(1–2), 9–23. doi: https://doi.org/10.1002/prca.201000058.
    CrossRef
26. Pontillo C.; Filip S.; Borràs DM.; Mullen W.; Vlahou A.; Mischak H. CE-MS-based proteomics in biomarker discovery and clinical application., Proteomics Clin Appl., 2015, 9(3–4), 322–334. doi: https://doi.org/10.1002/prca.201400115.
    CrossRef
27. Bhavyasri K.; Mounika G. Applications of CE-MS in pharmaceutical field., Int J Pharm Sci Res., 2020, 11(2), 563–572. doi: https://doi.org/10.13040/IJPSR.0975-8232.11(2).563-72.
    CrossRef
28. Skoog DA.; Holler FJ Crouch SR. Principles of Instrumental Analysis. 7th ed.; Boston, MA: Cengage Learning; 2017:490-560. ISBN: 9781337670074.
29. Wang J. Analytical Electrochemistry. 3rd ed.; Hoboken, NJ: Wiley; 2006:1-50. ISBN: 978-0-471-79030-3.
30. Nesměrák K.; Chalupa R. Polarography and pharmacy: A centenary of mutual interaction., Monatsh Chem., 2023, 154, 941–948. doi: https://doi.org/10.1007/s00706-023-03097-w.
    CrossRef
31. Page JE. Applications of polarography in pharmaceutical analysis., J Pharm Pharmacol., 1952, 4(1), 1–20. doi: https://doi.org/10.1111/j.2042-7158.1952.tb13105.x.
    CrossRef
32. Silver IA. Polarography and its biological applications., Phys Med Biol., 1967, 12(3), 285–299. doi: https://doi.org/10.1088/0031-9155/12/3/201.
    CrossRef
33. Turner APF. Biosensors: Sense and sensibility., Chem Soc Rev., 2013, 42(8), 3184–3196. doi: https://doi.org/10.1039/c3cs35528d.
    CrossRef
34. Patil AV.; Chuang YS.; Li C.; Wu CC. Recent advances in electrochemical immunosensors with nanomaterial assistance for signal amplification., Biosensors., 2023, 13, 125. doi: https://doi.org/10.3390/bios13010125.
    CrossRef
35. Song S.; Wang L.; Li J.; Fan C.; Zhao J. Aptamer-based biosensors., TrAC Trends Anal Chem., 2008, 27(2), 108–117. doi: https://doi.org/10.1016/j.trac.2007.12.004.
    CrossRef
36. Yetisen AK.; Akram MS.; Lowe CR. Paper-based microfluidic point-of-care diagnostic devices., Lab Chip., 2013, 13, 2210–2251. doi: https://doi.org/10.1039/C3LC50169H.
    CrossRef
37. Breadmore MC.; Grochocki W.; Kalsoom U.; Alves MN.; Phung SC.; Rokh MT.; Cabot JM.; Ghiasvand A.; Li F.; Shallan AI.; Keyon AS.; Alhusban AA.; See HH.; Wuethrich A.; Dawod M.; Quirino JP. Recent advances in enhancing the sensitivity of electrophoresis and electrochromatography in capillaries and microchips (2016–2018)., Electrophoresis., 2019, 40(1), 17–39. doi: https://doi.org/10.1002/elps.201800384.
    CrossRef
38. Shah M.; Patel N.; Tripathi N.; Vyas VK. Capillary electrophoresis methods for impurity profiling of drugs: A review of the past decade., J Pharm Anal., 2022, 12(1), 15–28. doi: https://doi.org/10.1016/j.jpha.2021.06.009.
    CrossRef
39. Voeten RLC.; Ventouri IK.; Haselberg R.; Somsen GW. Capillary electrophoresis: Trends and recent advances., Anal Chem., 2018, 90, 1464–1481. doi: https://doi.org/10.1021/acs.analchem.8b00015.
    CrossRef
40. Dawod M.; Arvin NE.; Kennedy RT. Recent advances in protein analysis by capillary and microchip electrophoresis., Analyst., 2017, 142(11), 1847–1866. doi: https://doi.org/10.1039/C7AN00198C.
    CrossRef
41. Darwish IA.; Amer SM.; Abdine HH.; Al-Rayes LI. New spectrophotometric and fluorimetric methods for determination of fluoxetine in pharmaceutical formulations., Int J Anal Chem., 2009, 257306, 1–9. doi: https://doi.org/10.1155/2009/257306.
    CrossRef
42. Gegechkori VI.; Shatilina AA.; Shulga NA.; Kuzina VN.; Vas’kova LB., Zenin VA., Levko AA. Application of fluorimetry for the determination of impurities in pancreatin in developing a reference standard., Pharm Chem J., 2023, 56, 1502–1505. doi: https://doi.org/10.1007/s11094-023-02821-4.
    CrossRef
43. Li W.; Cao LX.; Li GR.; Jin WJ. Applications of phosphorimetry in pharmaceutical analysis., Guang Pu Xue Yu Guang Pu Fen Xi., 2002, 22(3), 518–522.
44. Qarah N, El-Maaiden E. Spectrophotometric/titrimetric drug analysis. In: Shukla R, Kuznetsov A, Ali A, editors. Drug Formulation Design. 2023:3-12. Doi: https://doi.org/10.5772/intechopen.109364.
    CrossRef
45. Siddiqui MR.; Alothman ZA.; Rahman N. Analytical techniques in pharmaceutical analysis: a review., Arab J Chem., 2017, 10(Suppl. 1), S1409–S1421. doi: https://doi.org/10.1016/j.arabjc.2013.04.016.
    CrossRef
46. Claridge TDW. High-Resolution NMR Techniques in Organic Chemistry. 3rd ed. Elsevier; 2016:1-80. ISBN: 978-0-08-099986-9.
47. Smith E, Dent G. Modern Raman Spectroscopy: A Practical Approach. 2nd ed. Wiley; 2019:1-50. ISBN: 0-471-49794-0.
48. Fasman GD. Circular Dichroism and the Conformational Analysis of Biomolecules. Springer; 1996:1-30. ISBN: 978-0-306-45142-3.
49. Schlücker S. Surface-enhanced Raman spectroscopy: concepts and chemical applications., Angew Chem Int Ed., 2014, 53(19), 4756–4795. doi: https://doi.org/10.1002/anie.201205748.
    CrossRef
50. Nara SN.; Ojha RR.; Hamrapurkar PD. A review on the liquid chromatography–nuclear magnetic resonance (LC–NMR) and its application in pharmacy., World J Pharm Res., 2020, 9(12), 647–662. doi: https://doi.org/10.20959/wjpr202012-18839.
51. Walker GS.; O’Connell TN. Comparison of LC–NMR and conventional NMR for structure elucidation in drug metabolism studies., Expert Opin Drug Metab Toxicol., 2008, 4(10), 1295–1305. doi: https://doi.org/10.1517/17425255.4.10.1295.
    CrossRef
52. Lequin RM. Enzyme immunoassay (EIA)/enzyme-linked immunosorbent assay (ELISA)., Clin Chem., 2005, 51(12), 2415–2418. doi: https://doi.org/10.1373/clinchem.2005.051532.
    CrossRef
53. Yalow RS.; Berson SA. Immunoassay of endogenous plasma insulin., J Clin Invest., 1960, 39(7), 1157–1175. doi: https://doi.org/10.1172/JCI104363.
    CrossRef
54. Wild D. The Immunoassay Handbook. 4th ed. Elsevier; 2013:1-40. Doi: https://doi.org/10.1016/C2010-0-66244-4.
    CrossRef
55. Valones MAA.; Guimarães RL.; Brandão LAC.; de Souza PRE.; Carvalho AAT.; Crovela S. Principles and applications of polymerase chain reaction in medical diagnostic fields: a review., Braz J Microbiol., 2009, 40, 1–11. doi: https://doi.org/10.1590/S1517-83822009000100001.
    CrossRef
56. Maheaswari R.; Kshirsagar JT.; Lavanya N. Polymerase chain reaction: a molecular diagnostic tool in periodontology., J Indian Soc Periodontol., 2016, 20(2), 128–135. doi: https://doi.org/10.4103/0972-124X.176391.
    CrossRef
57. Niemeyer CM.; Adler M.; Wacker R. Immuno-PCR: high sensitivity detection of proteins by nucleic acid amplification., Trends Biotechnol., 2005, 23(4), 208–216. doi: https://doi.org/10.1016/j.tibtech.2005.02.006.
    CrossRef
58. Keustermans GCE.; Hoeks SBE.; Meerding JM.; Prakken BJ.; de Jager W. Cytokine assays: an assessment of the preparation and treatment of blood and tissue samples., Methods., 2013, 61(1), 10–17. doi: https://doi.org/10.1016/j.ymeth.2013.04.005.
    CrossRef
59. Rissin DM.; Kan CW.; Campbell TG.; Howes SC.; Fournier DR.; Song L.; Piech T.; Patel PP.; Chang L.; Rivnak AJ.; Ferrell EP.; Randall JD.; Provuncher GK.; Walt DR.; Duffy DC. Single-molecule enzyme-linked immunosorbent assay detects serum proteins at subfemtomolar concentrations., Nat Biotechnol., 2010, 28(6), 595–599. doi: https://doi.org/10.1038/nbt.1641.
    CrossRef
60. Takáts Z.; Wiseman JM.; Gologan B.; Cooks RG. Mass spectrometry sampling under ambient conditions with desorption electrospray ionization., Science., 2004, 306(5695), 471–473. doi: https://doi.org/10.1126/science.1104404.
    CrossRef
61. Park HM.; Kim HJ.; Jang YP.; Kim SY. Direct analysis in real time mass spectrometry (DART-MS) analysis of skin metabolome changes in the ultraviolet B-induced mice., Biomol Ther., 2013, 21(6), 470–475. doi: http://dx.doi.org/10.4062/biomolther.2013.071.
    CrossRef
62. Harkewicz R.; Dennis EA. Applications of mass spectrometry to lipids and membranes., Annu Rev Biochem., 2011, 80, 301–325. doi: https://doi.org/10.1146/annurev-biochem-060409-092612.
    CrossRef
63. Whitesides G. The origins and the future of microfluidics., Nature., 2006, 442, 368–373. doi: https://doi.org/10.1038/nature05058.
    CrossRef
64. Ekins S.; Puhl AC.; Zorn KM.; Lane TR.; Russo DP.; Klein JJ, Hickey AJ.; Clark AM. Exploiting machine learning for end-to-end drug discovery and development., Nat Mater., 2019; 18(5), 435–441. doi: https://doi.org/10.1038/s41563-019-0338-z.
    CrossRef
65. Nováková L. Challenges in the development of bioanalytical liquid chromatography–mass spectrometry., J Chromatogr A., 2013, 1292, 25–37. doi: https://doi.org/10.1016/j.chroma.2012.08.087.
    CrossRef
66. Pandey S.; Pandey P.; Tiwari G.; Tiwari R. Bioanalysis in drug discovery and development., Pharm Methods., 2010, 1(1), 14–24. doi: https://doi.org/10.4103/2229-4708.72223.
    CrossRef
67. Straubhaar J.; D’Souza A.; Niziolek Z.; Budnik B. Single cell proteomics analysis of drug response shows its potential as a drug discovery platform., Mol Omics., 2024, 20(1), 6–18. doi: https://doi.org/10.1039/D3MO00124E.
    CrossRef
68. Blas-Muñoz L.; Orrego AH.; Hofmeister M.; Martínez-Salvador J.; Ortega C.; Berrio VR, Díaz-Rullo J.; Finnigan J.; Charnock S.; Fessner W.-D.; González-Pastor JE.; Hidalgo A. A microfluidics-based ultrahigh-throughput screening unveils diverse ketoreductases relevant to pharmaceutical synthesis., Anal Chem., 2025, 97, 20698–20706. doi: https://doi.org/10.1021/acs.analchem.5c01029
    CrossRef
69. Acharya B.; Behera A.; Behera S. Optimizing drug discovery: surface plasmon resonance techniques and their multifaceted applications., Chem Phys Impact., 2024, 8, 100414. doi: https://doi.org/10.1016/j.chphi.2023.100414.
    CrossRef
70. Clark KD.; Zhang C.; Anderson JL. Sample preparation for bioanalytical and pharmaceutical analysis., Anal Chem., 2016, 88(23), 11262–11270. doi: https://doi.org/10.1021/acs.analchem.6b02935.
    CrossRef
71. Moein MM.; El Beqqali A.; Abdel-Rehim M. Bioanalytical method development and validation: Critical concepts and strategies., J Chromatogr B., 2017, 1043, 3–11. doi: https://doi.org/10.1016/j.jchromb.2016.09.028.
    CrossRef

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