Trends in Pharmaceutical Analysis: The Evolving Role of Liquid Chromatography

Analytical Chemistry, Год журнала: 2025, Номер unknown

Опубликована: Фев. 26, 2025

Язык: Английский

---

Ion exchange chromatography of biotherapeutics: Fundamental principles and advanced approaches

Journal of Chromatography A, Год журнала: 2025, Номер 1742, С. 465672 - 465672

Опубликована: Янв. 9, 2025

Ion exchange chromatography (IEX) is an important analytical technique for the characterization of biotechnology-derived products, such as monoclonal antibodies (mAbs) and more recently, cell gene therapy products messenger ribonucleic acid (mRNA) adeno-associated viruses (AAVs). This review paper first outlines basic principles separation mechanisms IEX charge variant biotherapeutics, examines different elution modes based on salt or pH gradients. It then highlights several recent trends when applying including: i) effective use gradients, ii) improvement selectivity by using organic solvents in mobile phase, multi-step combining ion pairing exchange, iii) increase throughput ultra-short columns automated screening conditions. The also discusses incorporation into multidimensional liquid setups, integrating it with other chromatographic dimensions analysis complex biotherapeutic products. covers coupling mass spectrometry (MS), mobility (IMS), multi-angle light scattering (MALS) to identify various species contained samples. In conclusion, considered today essential toolbox quality control offers a unique mechanism can be coupled highly informative detectors, MS MALS.

Язык: Английский

---

Retention modeling of therapeutic peptides in sub‐/supercritical fluid chromatography

Separation Science Plus, Год журнала: 2024, Номер 7(5)

Опубликована: Фев. 11, 2024

Abstract Chromatographic modeling software packages are valuable tools during method optimization steps. These well established for reversed‐phase applications utilizing retention time (RT) prediction to optimize separations and receive robust methods, which is of high interest the analysis pharmaceuticals. In contrast liquid chromatography, knowledge RT in supercritical fluid chromatography limited a manageable number studies. This study uses DryLab predict RTs peptides bacitracin (Bac), colistin, tyrothricin (Tyro), insulin analogs. Gradient time, column temperature, ternary composition (terC) carbon dioxide, methanol (MeOH), acetonitrile (ACN) gradient elution varied feasibility approach using neutral (Viridis BEH) an amino‐derivatized aromatic (Torus 2‐PIC) stationary phase. second part, chromatographic performed silico through adjustments separation fingerprint Bac. The final utilizes Viridis BEH (100 × 3.0 mm, 1.7 μm), modifier consisting ACN/MeOH/water/methanesulfonic acid (60:40:2:0.1, v:v:v:v). addition, changes order Tyro compounds with proportion terC combination Torus Diol investigated.

Язык: Английский

---

Improved assay development of pharmaceutical modalities using feedback-controlled liquid chromatography optimization

Journal of Chromatography A, Год журнала: 2024, Номер 1722, С. 464830 - 464830

Опубликована: Март 24, 2024

Язык: Английский

---

Machine learning models and performance dependency on 2D chemical descriptor space for retention time prediction of pharmaceuticals

Journal of Chromatography A, Год журнала: 2024, Номер 1730, С. 465109 - 465109

Опубликована: Июнь 18, 2024

Язык: Английский

---

In-silico method development and optimization of on-line comprehensive two-dimensional liquid chromatography via a shortcut model

Journal of Chromatography A, Год журнала: 2024, Номер 1721, С. 464818 - 464818

Опубликована: Март 21, 2024

Comprehensive two-dimensional liquid chromatography (LCxLC) represents a valuable alternative to conventional single column, or one-dimensional, (1D-LC) for resolving multiple components in complex mixture short time. However, developing LCxLC methods with trial-and-error experiments is challenging and time-consuming, which why the technique not dominant despite its significant potential. This work presents novel shortcut model in-silico predicting retention time peak width within an RPLCxRPLC separation system (i.e., systems that use reversed-phase columns (RPLC) both dimensions). Our computationally effective uses hydrophobic-subtraction (HSM) predict considers limitations due sample volume, undersampling maximum pressure drop. The used two-step strategy sample-dependent optimization of systems. In first step, Kendall's correlation coefficient all possible combinations available evaluated, best column pair selected accordingly. second optimal values design variables, flow rate, pH loop are obtained via multi-objective stochastic optimization. applied method development 8, 12 16 component mixtures. It shown proposed provides easy way accelerate full-comprehensive 2D-LC as it does require any experimental campaign entire run can take less than two minutes.

Язык: Английский

---

Chromatography Media and Purification Processes for Complex and Super-large Biomolecules: A Review

Journal of Chromatography A, Год журнала: 2025, Номер unknown, С. 465721 - 465721

Опубликована: Янв. 1, 2025

Язык: Английский

---

Two-Dimensional Size Exclusion-Reversed Phase Liquid Chromatography for Quantitative Analysis of L1 Proteins in Complex Vaccine Matrices

Опубликована: Янв. 1, 2025

Язык: Английский

---

Two-Dimensional Size Exclusion Reversed-Phase Liquid Chromatography for Quantitative Analysis of L1 Proteins in Complex Vaccine Matrices

Journal of Chromatography A, Год журнала: 2025, Номер 1748, С. 465851 - 465851

Опубликована: Март 7, 2025

Язык: Английский

---

Purification of Pharmaceuticals via Retention Time Prediction: Leveraging Graph Isomorphism Networks, Limited Data, and Transfer Learning

Journal of Separation Science, Год журнала: 2025, Номер 48(6)

Опубликована: Июнь 1, 2025

ABSTRACT The design‐make‐test cycle for drug discovery is highly dependent on the purification of synthesized compounds. Prior to evaluation suitability, ultrahigh‐performance liquid chromatography used an initial standard analysis, where retention times analytes are measured with a shorter gradient method and select appropriate gradients final method. To circumvent this preliminary screening experiment small molecule libraries, time prediction had been achieved previously by use commercial modeling methods. However, these models can have limited applicability when built from smaller datasets less effective constructed disparate data collected under differing conditions. Having thousands high‐throughput physiochemical screening, we sought leverage construction predictive enabling macrocyclic peptide libraries. Utilizing 4549 their structure‐to‐retention‐time model was using graph isomorphism network, form artificial neural network architecture. Once fitted data, re‐trained technique known as transfer learning. Through learning, training set 80 yielded that, evaluated against test 24 analytes, displays high performance metrics coefficient determination ( R 2 ) 0.82 mean average error 0.088 min, or 1.26% time. Comparatively, best quantitative structure‐retention relationship poorly performed, 0.11 0.202 min. This has deployed internally Dash app help democratize developed being selecting methods based analyte structure.

Язык: Английский

---