Everything below concerns HPLC. We keep the language plain, cite what the science says, and separate well-supported claims from open questions.
Last reviewed on 2026-05-30. Where a claim depends on a specific study, the study is described rather than over-claimed.
Quality control for HPLC testing combines scheduled checks, documented procedures, and review of results. Before sample analysis, system suitability testing confirms that the instrument, column, and method meet predefined criteria. Common criteria include resolution between critical peaks, retention time precision, peak tailing, and theoretical plate count. Failure triggers investigation before results are reported. Records link raw data, calculations, instrument logs, and analyst identity to each batch, supporting audits and repeat analysis.
Method validation evaluates accuracy, precision, specificity, linearity, range, detection limit, quantitation limit, and robustness. Regulatory guidance for pharmaceuticals, foods, and environmental testing defines expected documentation and acceptance criteria. Verification confirms that a validated method works in a specific laboratory with its own instruments and reagents. Calibration curves use reference standards with known purity and traceability, while measurement uncertainty is estimated from validation data, control charts, and collaborative studies. The scope of validation depends on the method's intended use.
Instrumentation includes a solvent delivery system, an autosampler, a column oven, and one or more detectors. Reversed-phase columns with chemically modified silica are widely used, but normal-phase, ion-exchange, size-exclusion, and affinity modes exist for specific separations. Detectors may rely on ultraviolet absorbance, fluorescence, refractive index, or mass spectrometry. Column temperature, mobile phase composition, and flow rate are adjusted to improve resolution. System pressure is monitored because rising pressure can indicate column blockage or deteriorating packing.
Separation performance depends on particle size, pore size, column length, and the chemistry of the stationary phase. Smaller particles generally improve efficiency but require higher pressure and suitable instrumentation. The mobile phase often contains buffers and organic solvents that influence retention and selectivity. Testing labs select conditions based on the analytes, sample matrix, and required sensitivity. Method development frequently involves screening several columns and solvent mixtures before a final set of conditions is chosen.
High-performance liquid chromatography is an analytical technique that separates components in a liquid sample by passing them through a packed column under pressure. A pump delivers a mobile phase at a controlled flow rate, and an injector introduces the sample into the stream. Differences in how analytes partition between the mobile phase and the stationary phase cause them to exit the column at different times. Detection then records a signal proportional to the amount of each separated substance. The resulting chromatogram provides retention times and peak areas for identification and quantification.
| Property | Value | Notes |
|---|---|---|
| Retention time RSD | ≤1% for five replicate injections | Typical criterion; method-specific limits apply. |
| Resolution | ≥1.5 between critical pair | Baseline separation is generally desired. |
| Tailing factor | ≤2.0 | Measures peak symmetry. |
| Theoretical plates | ≥2000 per column | Method-dependent; higher values indicate greater efficiency. |
| Peak area RSD | ≤2% for replicate injections | Reflects autosampler and detector precision. |
Developing an HPLC method begins with defining the purpose, such as quantifying a main component, measuring impurities, or confirming identity. Analysts select separation mode, column, mobile phase, detection, and sample preparation based on analyte properties and matrix. Experiments vary solvent strength, pH, buffer type, and temperature to achieve resolution between critical peaks. The goal is a robust method that produces reliable results across instruments and operators. Method development often involves trial runs and statistical optimization.
Validation demonstrates that a method is suitable for its intended use. Typical performance characteristics include accuracy, precision, specificity, linearity, range, limit of detection, limit of quantitation, and robustness. Regulators and standards organizations provide frameworks, but specific requirements depend on the application and jurisdiction. System suitability tests are run before sample analysis to confirm resolution, peak symmetry, retention time repeatability, and sensitivity. A validated method is not permanently fixed; changes may require partial or full revalidation.
Routine HPLC testing depends on controlled reagents, calibrated instruments, and documented procedures. Columns degrade over time, so retention times and peak shapes are monitored for drift. Mobile phases are filtered and degassed to prevent pump damage and detector noise. Reference standards must be traceable and stored under suitable conditions. Data handling systems record injections, calculations, and audit trails. Quality control samples interspersed with unknowns help detect errors during a run.
High-performance liquid chromatography is an analytical technique that separates components in a liquid sample. A pump moves a liquid mobile phase through a column packed with a solid stationary phase. Compounds interact differently with both phases and travel at different rates, leaving the column at distinct retention times. A detector records these arrivals as peaks on a chromatogram. The resulting pattern supports identification and quantification of substances in mixtures. Modern instruments use high pressure to force solvent through small particles, which improves speed and resolution compared with older low-pressure liquid chromatography methods.
Separation in HPLC depends on the chemistry of the stationary phase, the composition of the mobile phase, and the physical properties of the column. Reverse-phase separations use a nonpolar stationary phase and a polar mobile phase, and they are common for many organic compounds. Ion-exchange, size-exclusion, and normal-phase modes serve other classes of analytes. Gradient elution changes solvent strength over time, while isocratic elution holds it constant. Flow rate, temperature, particle size, and column length all influence peak shape and resolution. Detection may use ultraviolet absorbance, fluorescence, refractive index, or mass spectrometry, depending on the analyte and the required sensitivity.
Routine HPLC testing compares a sample result with a calibration curve prepared from known reference standards. Peak area or peak height is plotted against concentration, and the curve is used to estimate unknown amounts. Retention time supports tentative identification when compared with a standard, though mass spectrometry or another confirmatory method may be needed for definitive identification. Pre-run checks verify repeatability, resolution, and peak symmetry before sample analysis. Limits of detection and quantification describe the smallest amounts that can be reliably observed or measured. Sample preparation, filtration, and degassing help prevent column damage and inconsistent results.
Method validation demonstrates that an analytical procedure is suitable for its intended purpose. Typical validation characteristics include accuracy, precision, specificity, linearity, range, limit of detection, limit of quantitation, and robustness. Regulatory guidance from bodies such as the International Council for Harmonisation and the United States Pharmacopeia outlines expectations, though specific criteria depend on the product and method. System suitability tests are run before sample analysis to confirm resolution, peak symmetry, column efficiency, and injection repeatability. Failure of these checks can invalidate a batch of measurements.
Practical HPLC testing depends on careful sample preparation and instrument maintenance. Samples may require filtration, dilution, pH adjustment, or extraction to avoid column damage and matrix interference. Mobile phases are degassed and filtered, and columns are equilibrated before injection. Common problems include peak tailing, baseline drift, ghost peaks, carryover, and co-elution of analytes. Documentation of instrument logs, calibration records, and electronic audit trails supports data integrity and traceability. Ongoing training and routine maintenance help reduce variability between analysts and laboratories.
RGD and other bioactive ligands can be presented on the surface of a biomaterial in a number of different spatial arrangements, and it has been demonstrated that these arrangements have a significant impact on cell behavior. In self-assembled monolayers, it was found that adhesion and proliferation of both human umbilical vein endothelial cells (HUVECs) and human mesenchymal stem cells (MSCs) increased as a function of RGD peptide density. These studies also showed that RGD density could change integrin expression, which has been postulated to enable control of biochemical signaling pathways. Further investigation of MSCs on self-assembled monolayers showed that modulating RGD density and the affinity of RGD for αvβ3 (through use of linear and cyclized RGD) could be used to control the differentiation of MSCs. The effect of RGD presentation on cells in 3D biomaterials, which more accurately replicate the in vivo environment, has also been evaluated. In degradable polyethylene glycol hydrogels, the length of capillary-like structures formed by HUVECs was directly proportional to the density of RGD in the hydrogel. Additionally, studies in nano-patterning have shown that, whereas an increase in global RGD density increases cell adhesion strength until saturation, an increase in local (mico/nano-scale) RGD density does not follow this trend.
Christopher A. Lipinski is a medicinal chemist who is working at Pfizer, Inc. He is known for his "rule of five", an algorithm that predicts drug compounds that are likely to have oral activity. By the number of citations, he is the most cited author of some pharmacology journals: Journal of Pharmacological and Toxicological Methods, Advanced Drug Delivery Reviews, Drug Discovery Today: Technologies. Lipinski received his PhD from the University of California, Berkeley in 1968 in physical organic chemistry. The Advanced Drug Delivery Reviews article reporting his "rule of five" is one of the most cited publications in the journal's history. In 2006, he received an honorary law degree from the University of Dundee and he has won various awards, including being the Society for Biomolecular Sciences' winner of the 2006 SBS Achievement Award for Innovation in HTS.
Studies in the hematopoietic system disclosed that during endothelial to hematopoietic stem cell transition, ADGRG1 is a transcriptional target of the heptad complex of hematopoietic transcription factors, and is required for hematopoietic cluster formation. Recently, two studies showed that ADGRG1, is a cell autonomous regulator of oligodendrocyte development through Gα12/13 proteins and Rho activation. Della Chiesa et al. demonstrate that ADGRG1 is expressed on CD56dull natural killer (NK) cells. Lin and Hamann's group show all human cytotoxic lymphocytes, including CD56dull NK cells and CD27–CD45RA+ effector-type CD8+ T cells, express ADGRG1.
Biochemical differences between different organisms and humans are useful for drug development. For instance, penicillin kills bacteria by inhibiting the bacterial enzyme DD-transpeptidase, destroying the development of the bacterial cell wall and inducing cell death. Thus, the study of binding sites is relevant to many fields of research, including cancer mechanisms, drug formulation, and physiological regulation. The formulation of an inhibitor to mute a protein's function is a common form of pharmaceutical therapy.
Sources: en.wikipedia.org
Aaron R. Wheeler is a Canadian chemist who is a professor of chemistry and biomedical engineering at the University of Toronto since 2005 with cross-appointment at Institute of Biomedical Engineering and Terrence Donnelly Centre for Cellular and Biomolecular Research. His academic laboratory is located at Lash Miller Chemical Laboratories and Terrence Donnelly Centre for Cellular and Biomolecular Research at the University of Toronto. In 2005, Wheeler was appointed as assistant professor and Tier II Canada Research Chair then promoted to associate professor in 2010, full professor in 2013, and in 2018 he became the Tier I Canada Research Chair in Microfluidic Bioanalysis. Wheeler did his undergraduate studies at Furman University in Greenville, SC then he joined Stanford University from 1997 to 2003 to obtain his Ph.D. in chemistry under supervision of Richard Zare . Following graduation, he took a two-year NIH postdoctoral fellowship at UCLA till 2005.
failure mode and effects analysis (FMEA) manual statistical process control (SPC) manual measurement systems analysis (MSA) manual production part approval process (PPAP) manual APQP serves as a guide in the development process and also a standard way to share results between suppliers and automotive companies. APQP specifies three phases: Development, Industrialization, and Product Launch. Through these phases, 23 main topics will be monitored. These topics must be completed before the production is started. They include the following aspects: design robustness, design testing, and specification compliance, production process design, quality inspection standards, process capability, production capacity, product packaging, product testing, and operator training plan. These activities are sometimes carried out by third-party inspection and quality control companies such as SGS, Bureau Veritas, or QCADvisor, which provide on-site inspections, audits, and testing services to support APQP compliance. APQP focuses on:
There are numerous theories as to the exact cause and mechanism in type 2 diabetes. Central obesity is known to predispose individuals for insulin resistance. Abdominal fat is especially active hormonally, secreting a group of hormones called adipokines that may possibly impair glucose tolerance. But adiponectin, an anti-inflammatory adipokine, which is found in lower concentration in obese and diabetic individuals has shown to be beneficial and protective in type 2 diabetes mellitus (T2DM). Insulin resistance is a major feature of diabetes mellitus type 2, and central obesity is correlated with both insulin resistance and T2DM itself. Increased adiposity (obesity) raises serum resistin levels, which in turn directly correlate to insulin resistance. Studies have also confirmed a direct correlation between resistin levels and T2DM. And it is waistline adipose tissue (central obesity) which seems to be the foremost type of fat deposits contributing to rising levels of serum resistin. Conversely, serum resistin levels have been found to decline with decreased adiposity following medical treatment.
Sources: en.wikipedia.org
BLAST is an algorithm for comparing biomacromolecule primary structure, most often nucleotide sequence of DNA/RN, and amino acid sequence of proteins, stored in the bioinformatic databases, with the query sequence. The algorithm uses scoring of the available sequences against the query by a scoring matrix such as BLOSUM 62. The highest scoring sequences represent the closest relatives of the query, in terms of functional and evolutionary similarity. The database search by BLAST requires input data to be in a correct format (e.g. FASTA, GenBank, PIR or EMBL format). Users may also designate the specific databases to be searched, select scoring matrices to be used and other parameters prior to the tool run. The best hits in the BLAST results are ordered according to their calculated E-value (the probability of the presence of a similarly or higher-scoring hit in the database by chance).
Asparagine peptide lyase are one of the seven groups in which proteases, also termed proteolytic enzymes, peptidases, or proteinases, are classified according to their catalytic residue. The catalytic mechanism of the asparagine peptide lyases involves an asparagine residue acting as nucleophile to perform a nucleophilic elimination reaction, rather than hydrolysis, to catalyse the breaking of a peptide bond. The existence of this seventh catalytic type of proteases, in which the peptide bond cleavage occurs by self-processing instead of hydrolysis, was demonstrated with the discovery of the crystal structure of the self-cleaving precursor of the Tsh autotransporter from E. coli. These enzymes are synthesized as precursors or propeptides, which cleave themselves by an autoproteolytic reaction. The self-cleaving nature of asparagine peptide lyases contradicts the general definition of an enzyme given that the enzymatic activity destroys the enzyme. However, the self-processing is the action of a proteolytic enzyme, notwithstanding the enzyme is not recoverable from the reaction.
DHFR has been used as a tool to detect protein–protein interactions in a protein-fragment complementation assay (PCA), using a split-protein approach. DHFR-lacking CHO cells are the most commonly used cell line for the production of recombinant proteins. These cells are transfected with a plasmid carrying the dhfr gene and the gene for the recombinant protein in a single expression system, and then subjected to selective conditions in thymidine-lacking medium. Only the cells with the exogenous DHFR gene along with the gene of interest survive. Supplementation of this medium with methotrexate, a competitive inhibitor of DHFR, can further select for those cells expressing the highest levels of DHFR, and thus, select for the top recombinant protein producers. Dihydrofolate reductase has been shown to interact with GroEL and Mdm2. Click on genes, proteins and metabolites below to link to respective articles.
Sources: en.wikipedia.org
System suitability is typically performed before each batch or according to the validated method and laboratory procedure. Some long runs include periodic checks during analysis. The required frequency depends on regulatory expectations and method performance.
Retention time drift can result from changes in mobile phase composition, column temperature, pump flow, or column age. A gradual shift often points to column degradation. A sudden shift may indicate a leak, mixing error, or incorrect mobile phase.
Retention time alone cannot confirm identity because different compounds may elute at similar times. Coupling HPLC with mass spectrometry or comparing against authenticated standards increases confidence. Confirmation usually requires orthogonal data.
HPLC separates and detects individual compounds in a liquid sample, producing peaks at characteristic retention times. Peak area or height can be used to estimate concentration when calibrated with known standards. It does not identify unknown compounds with certainty unless additional detectors or reference materials are used.