FAQ Center

Most research and pharmaceutical laboratories have largely adopted lab automation hence removing the need for human input, especially in repetitive tasks/routines. With the integration of data science in lab automation, these automation systems have become ‘intelligent’.

A digital lab is a laboratory that has digitalized virtually all of its operations including supply chains, lab systems, personnel, and instruments. The laboratory, therefore, becomes more automated and smart. The streamlined operations make the lab more efficient while lab data is managed by an information management system.

The lab of the future will be centered on technology and data. Most of the laboratory operations will be automated using technology, especially robotics. The automated system will churn data that will be managed by an integrated information system that has data management and analytics tools.

Lab compliance is the practice of following a set of standards created for the safe and effective operation of a laboratory. Laboratory compliance standards cover a wide array of functionalities, including the management of funds and issues related to fraud, waste, communication, training, maintenance, and record-keeping. 

Data integrity is the overall quality and reliability of data. Maintaining accurate, reliable, and consistent data helps to save on time, energy, and costs.

Scientific data management systems (SDMS) are digital tools for the safety and accessibility of scientific data. These systems allow for more efficient data organization and analysis.

The difference between open and FAIR data is the degree of accessibility and requirements for usability. Open data is available without restriction, and FAIR data defines specific conditions to be accessed and utilized. FAIR stands for findable, accessible, interoperable, and reusable.

Calibration is important in the scientific laboratory as the accuracy of measurements collected with laboratory equipment is only reliable when instruments are properly calibrated. Calibration is performed to maintain the accuracy, standardization, and reproducibility of measurements to assure reliable benchmarking and results.

Data cleaning, often referred to as data cleansing or data scrubbing, is the process of repairing or removing incorrect, corrupted, repeated, or incomplete data within a dataset. If data is inaccurate, conclusions and outcomes are unreliable, which can affect efficiency, productivity, and profitability.

Assay development is a procedure employed in molecular biology to test or measure the activity of a drug in an organism. Assays are investigative procedures that qualitatively assess a compound or examine its effect on a molecular target. The development of successful assays is critical in drug discovery and development, with better assays increasing confidence in the productive and successful realization of new drugs. 

FAIR data principles are guidelines to improve the findability, accessibility, interoperability, and reusability of digital assets.  These guidelines aim to maximize the usefulness of digital assets by mandating usability requirements that go beyond permission alone. Unlike other types of data which follow different principles like open data, FAIR data prioritizes usability over maximizing openness. These usability requirements optimize the impact of scientific data and promote its preservation and utility.

GxP compliance is a series of regulatory and quality guidelines created to ensure that life science products are safe and that the integrity of supporting data is upheld and maintained. Compliance with GxP helps to ensure the traceability, accountability, and integrity of the data supporting life science products and services. 

Scientific data visualization is the process of converting often large and complex data sets into easily understandable and actionable states.

DNA sequencing is the process of deciphering the building blocks in a strand of DNA. This process can be performed using several different techniques that typically involve repeating one or more of the following steps: Replication Separation Measurement

There are four major steps in the drug discovery process: early drug discovery, pre-clinical phase, clinical phase, and regulatory approval.  Phase 1: Early drug discovery The early drug discovery phase uses many varying actions and tests, with researchers collaborating to identify and optimize prospected leads to a specific target. 

Small molecule drugs are organic compounds that affect a variety of molecular pathways, primarily by targeting key proteins. Such compounds have a low molecular weight, allowing them to easily penetrate cells. Small drugs are developed from leads that derive from rational drug design or isolated from nature. 

Hit to lead (H2L) is one of the key stages of the early drug discovery process. Hits are chemical compounds with a desired therapeutic effect at a known target molecule. The lead is the product of the screening process, useful in advanced stages of drug discovery.

Drug discovery is a long process that flows from target validation to clinical development, passing through the hit-to-lead stage. The early stages of drug development usually involve screening for active compounds.

One of the ways our body’s immune system protects itself from dangerous substances is by producing antibodies. Antibodies are a type of protein that attaches to another form of a protein called antigens. By finding and attaching to antigens, antibodies can impact other parts of the immune system to eliminate cells associated with the given antigen. 

CAR T-Cell therapy is a type of cancer treatment where a patient’s T cells are modified in the laboratory so that they will attack cancer cells. T-cells are a type of white blood cell that work with macrophages, which is a type of white blood cell related to infection.

CAR T-cell therapy is a powerful type of cancer treatment that uses modified immune cells to target and attack cancer cells. While potentially life-saving, CAR T-cell therapy is not always the most appropriate treatment. Depending on the circumstance, CAR T-cell therapy may be considered unsafe and reserved only for some patients.

Cheminformatics, or chemoinformatics, combines chemistry, computer science, and information science to organize, process, and present chemical data. As a field fundamentally linked to computer science, cheminformatics by its definition uses digital tools for its function and use. 

The best cancer treatment depends on the type of cancer, the stage of its development, and the health and makeup of the patient. While chemotherapy has remained one of the most common types of cancer treatments, new therapies are also bringing benefits to patients. 

Antibody sequences are important for engineering antibodies and characterizing proteins.  Knowing antibody sequences allows for rational experimental design for the development of potentially lifesaving therapeutics. Successful and efficient antibody sequencing requires the assistance of digital tools, and these tools are referred to as antibody sequence analysis software. 

The structure-activity relationship (SAR) is the relationship between the structure and biological activity of a molecule.  Analyzing the SAR for a given molecule allows researchers to identify the chemical group responsible for provoking a biological effect of interest. An understanding of this chemistry allows for changing the type of effect or strength of drugs by altering the chemical structure and the biological activity.