Wednesday, 29 July 2026

CMO vs. CDMO vs. CRO

A **Pharmaceutical CMO (Contract Manufacturing Organization)** is an external service provider that handles the production, formulation, and packaging of pharmaceutical products on behalf of drug developers or biotech companies.
Instead of investing heavily in building and maintaining specialized manufacturing facilities, pharmaceutical firms outsource production to CMOs to lower capital costs and speed up time-to-market.
## Core Services Offered by a CMO
 * **API Synthesis:** Producing Active Pharmaceutical Ingredients (the chemical or biological components that drive the therapeutic effect).
 * **Formulation & Dosage Manufacturing:** Transforming raw ingredients into final dosage forms (e.g., tablets, capsules, injectables, ointments, or biologics).
 * **Secondary Packaging & Labeling:** Managing serialisation, blister packaging, bottling, and regulatory-compliant labeling.
 * **Quality Assurance & Regulatory Compliance:** Adhering to strict **cGMP (current Good Manufacturing Practices)** standards required by regulatory bodies like the FDA or EMA.
## CMO vs. CDMO vs. CRO: Key Differences
It's common to see CMOs mentioned alongside similar acronyms in the pharma supply chain:
| Model | Full Name | Primary Focus |
|---|---|---|
| **CRO** | Contract Research Organization | Early-stage R&D, preclinical testing, and clinical trial design/management. |
| **CMO** | Contract Manufacturing Organization | Commercial-scale production based on client specifications. |
| **CDMO** | Contract Development & Manufacturing Organization | **End-to-end support:** Pre-formulation, process development, clinical batching, *and* commercial production under one roof. |
## Key Benefits of Partnering with a CMO
 1. **Capital Efficiency:** Eliminates the tens of millions of dollars needed to build specialized cleanrooms, isolators, or bioreactors.
 2. **Scalability & Agility:** Easily scale production up or down depending on market demand without maintaining excess facility capacity.
 3. **Specialized Expertise:** Access niche technologies (e.g., sterile aseptic filling, highly potent API handling) without hiring dedicated internal teams.
 4. **Strategic Focus:** Allows drug discovery companies to focus purely on R&D, clinical trials, and marketing.



clinical trials-designs; active comparator studies; protocol; design features; placebo groups;phases;length;administration;ethical conduct; commercial ties and unfavourable studies; safety; sponsor; local site investigators; IRBs; regulatory agencies;

A fundamental distinction in evidence-based medicine is between observational studies and randomized controlled trials. Types of observational studies in epidemiology, such as the cohort study and the case-control study, provide less compelling evidence than the randomized controlled trial. In observational studies, the investigators only observe associations (correlations) between the treatments experienced by participants and their health status or diseases. However, under certain conditions, causal effects can be inferred from these studies.
Under the right conditions, a randomized controlled trial can provide compelling evidence that the study treatment causes an effect on human health.
Currently, some Phase 2 and most Phase 3 drug trials are designed as randomized, double-blind, and placebo-controlled.
  • Randomized: Each study subject is randomly assigned to receive either the study treatment or a placebo.
  • Blind: The subjects involved in the study do not know which study treatment they receive. If the study is double-blind, the researchers also do not know which treatment is being given to any given subject. This 'blinding' is to prevent biases, since if a physician knew which patient was getting the study treatment and which patient was getting the placebo, he/she might be tempted to give the (presumably helpful) study drug to a patient who could more easily benefit from it. In addition, a physician might give extra care to only the patients who receive the placebos to compensate for their ineffectiveness. A form of double-blind study called a "double-dummy" design allows additional insurance against bias or placebo effect. In this kind of study, all patients are given both placebo and active doses in alternating periods of time during the study.
  • Placebo-controlled: The use of a placebo (fake treatment) allows the researchers to isolate the effect of the study treatment from the placebo effect.
Although the term "clinical trials" is most commonly associated with the large, randomized studies typical of Phase 3, many clinical trials are small. They may be "sponsored" by single physicians or a small group of physicians, and are designed to test simple questions. In the field of rare diseases, sometimes the number of patients might be the limiting factor for a clinical trial. Other clinical trials require large numbers of participants (who may be followed over long periods of time), and the trial sponsor is a private company, a government health agency, or an academic research body such as a university.

Active comparator studies

Of note, during the last 10 years or so, it has become a common practice to conduct "active comparator" studies (also known as "active control" trials). In other words, when a treatment is clearly better than doing nothing for the subject (i.e. giving them the placebo), the alternate treatment would be a standard-of-care therapy. The study would compare the 'test' treatment to standard-of-care therapy.
A growing trend in the pharmacology field involves the use of third-party contractors to obtain the required comparator compounds. Such third parties provide expertise in the logistics of obtaining, storing, and shipping the comparators. As an advantage to the manufacturer of the comparator compounds, a well-established comparator sourcing agency can alleviate the problem of parallel importing (importing a patented compound for sale in a country outside the patenting agency's sphere of influence).[citation needed]

Clinical trial protocol

A clinical trial protocol is a document used to gain confirmation of the trial design by a panel of experts and adherence by all study investigators, even if conducted in various countries.
The protocol describes the scientific rationale, objective(s), design, methodology, statistical considerations, and organization of the planned trial. Details of the trial are also provided in other documents referenced in the protocol, such as an investigator's brochure.
The protocol contains a precise study plan for executing the clinical trial, not only to assure safety and health of the trial subjects, but also to provide an exact template for trial conduct by investigators at multiple locations (in a "multicenter" trial) to perform the study in exactly the same way. This harmonization allows data to be combined collectively as though all investigators (referred to as "sites") were working closely together. The protocol also gives the study administrators (often a contract research organization), as well as the site team of physicians, nurses and clinic administrators, a common reference document for site responsibilities during the trial.
The format and content of clinical trial protocols sponsored by pharmaceutical, biotechnology or medical device companies in the United States, European Union, or Japan have been standardized to follow Good Clinical Practice guidance[15] issued by the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH).[16] Regulatory authorities in Canada and Australia also follow ICH guidelines. Some journals, e.g. Trials, encourage trialists to publish their protocols in the journal.

Design features

Informed consent

An essential component of initiating a clinical trial is to recruit study subjects following procedures using a signed document called "informed consent".[17] Generally, children participating in clinical trial cannot autonomously provide informed consent, but depending on their age and other factors, may be required to provide informed assent.
Informed consent is a legally defined process of a person being told about key facts involved in a clinical trial before deciding whether or not to participate. To fully describe participation to a candidate subject, the doctors and nurses involved in the trial explain the details of the study using terms the person will understand. Foreign language translation is provided if the participant's native language is not the same as the study protocol.
The research team provides an informed consent document that includes trial details, such as its purpose, duration, required procedures, risks, potential benefits and key contacts. The participant then decides whether or not to sign the document in agreement. Informed consent is not an immutable contract, as the participant can withdraw at any time without penalty.

Statistical power

The number of patients enrolled in a study has a large bearing on the ability of the study to reliably detect the size of the effect of the study intervention. This is described as the "power" of the trial. The larger the sample size or number of participants in the trial, the greater the statistical power.
However, in designing a clinical trial, this consideration must be balanced with the fact that more patients make for a more expensive trial. The power of a trial is not a single, unique value; it estimates the ability of a trial to detect a difference of a particular size (or larger) between the treated (tested drug/device) and control (placebo or standard treatment) groups. By example, a trial of a lipid-lowering drug versus placebo with 100 patients in each group might have a power of 0.90 to detect a difference between patients receiving study drug and patients receiving placebo of 10 mg/dL or more, but only have a power of 0.70 to detect a difference of 5 mg/dL.

Placebo groups

Merely giving a treatment can have nonspecific effects, and these are controlled for by the inclusion of a placebo group. Subjects in the treatment and placebo groups are assigned randomly and blinded as to which group they belong. Since researchers can behave differently to subjects given treatments or placebos, trials are also doubled-blinded so the researchers do not know to which group a subject is assigned.
Assigning a person to a placebo group can pose an ethical problem if it violates his or her right to receive the best available treatment. The Declaration of Helsinki provides guidelines on this issue.

Phases

Clinical trials involving new drugs are commonly classified into four phases. Each phase of the drug approval process is treated as a separate clinical trial. The drug-development process will normally proceed through all four phases over many years. If the drug successfully passes through Phases 0, 1, 2, and 3, it will usually be approved by the national regulatory authority for use in the general population.
  • Phase 0: Pharmacodynamics and Pharmacokinetics
  • Phase 1: Screening for safety
  • Phase 2: Establishing the efficacy of the drug, usually against a placebo
  • Phase 3: Final confirmation of safety and efficacy
  • Phase 4: Sentry studies during sales
Each phase has a different purpose and helps scientists answer a different question:
In Phase 0 trials are the first-in-human trials. Single subtherapeutic doses of the study drug are given to a small number of subjects (10 to 15) to gather preliminary data on the agent's pharmacodynamics (what the drug does to the body) and pharmacokinetics (what the body does to the drugs).[18]
In Phase 1 trials, researchers test an experimental drug or treatment in a small group of people (20-80) for the first time to evaluate its safety, determine a safe dosage range, and identify side effects.
In Phase 2 trials, the experimental treatment is given to a larger group of people (100-300) to see if it is effective and to further evaluate its safety.
In Phase 3 trials, the treatment is given to large groups of people (1,000-3,000) to confirm its effectiveness, monitor side effects, compare it to commonly used treatments, and collect information that will allow it to be used safely.
In Phase 4 trials, postmarketing studies delineate additional information, including the treatment's risks, benefits, and optimal use.
Before pharmaceutical companies start clinical trials on a drug, they conduct extensive preclinical studies.

Length

Clinical trials are only a small part of the research that goes into developing a new treatment. Potential drugs, for example, first have to be discovered, purified, characterized, and tested in labs (in cell and animal studies) before ever undergoing clinical trials. In all, about 1,000 potential drugs are tested before just one reaches the point of being tested in a clinical trial.[citation needed] For example, a new cancer drug has, on average, six years of research behind it before it even makes it to clinical trials. But the major holdup in making new cancer drugs available is the time it takes to complete clinical trials themselves. On average, about eight years pass from the time a cancer drug enters clinical trials until it receives approval from regulatory agencies for sale to the public.[19] Drugs for other diseases have similar timelines.
Some reasons a clinical trial might last several years:
  • For chronic conditions such as cancer, it takes months, if not years, to see if a cancer treatment has an effect on a patient.
  • For drugs that are not expected to have a strong effect (meaning a large number of patients must be recruited to observe 'any' effect), recruiting enough patients to test the drug's effectiveness (i.e., getting statistical power) can take several years.
  • Only certain people who have the target disease condition are eligible to take part in each clinical trial. Researchers who treat these particular patients must participate in the trial. Then they must identify the desirable patients and obtain consent from them or their families to take part in the trial.
The biggest barrier to completing studies is the shortage of people who take part. All drug and many device trials target a subset of the population, meaning not everyone can participate. Some drug trials require patients to have unusual combinations of disease characteristics. It is a challenge to find the appropriate patients and obtain their consent, especially when they may receive no direct benefit (because they are not paid, the study drug is not yet proven to work, or the patient may receive a placebo). In the case of cancer patients, fewer than 5% of adults with cancer will participate in drug trials. According to the Pharmaceutical Research and Manufacturers of America (PhRMA), about 400 cancer medicines were being tested in clinical trials in 2005. Not all of these will prove to be useful, but those that are may be delayed in getting approved because the number of participants is so low.[20]
For clinical trials involving a seasonal indication (such as airborne allergies, seasonal affective disorder, influenza, and others), the study can only be done during a limited part of the year (such as spring for pollen allergies), when the drug can be tested. This can be an additional complication on the length of the study, yet proper planning and the use of trial sites in the Southern, as well as the Northern Hemisphere allows for year-round trials, which can reduce the length of the studies.[21][22]
Clinical trials that do not involve a new drug usually have a much shorter duration. (Exceptions are epidemiological studies, such as the Nurses' Health Study.

Administration

Clinical trials designed by a local investigator, and (in the US) federally funded clinical trials, are almost always administered by the researcher who designed the study and applied for the grant. Small-scale device studies may be administered by the sponsoring company. Clinical trials of new drugs are usually administered by a contract research organization (CRO) hired by the sponsoring company. The sponsor provides the drug and medical oversight. A CRO is contracted to perform all the administrative work on a clinical trial. For Phases 2, 3 and 4, the CRO recruits participating researchers, trains them, provides them with supplies, coordinates study administration and data collection, sets up meetings, monitors the sites for compliance with the clinical protocol, and ensures the sponsor receives data from every site. Specialist site management organizations can also be hired to coordinate with the CRO to ensure rapid IRB/IEC approval and faster site initiation and patient recruitment. Phase 1 clinical trials of new medicines are often conducted in a specialist clinical trial clinic, with dedicated pharmacologists, where the subjects can be observed by full-time staff. These clinics are often run by a CRO which specialises in these studies.
At a participating site, one or more research assistants (often nurses) do most of the work in conducting the clinical trial. The research assistant's job can include some or all of the following: providing the local institutional review board (IRB) with the documentation necessary to obtain its permission to conduct the study, assisting with study start-up, identifying eligible patients, obtaining consent from them or their families, administering study treatment(s), collecting and statistically analyzing data, maintaining and updating data files during followup, and communicating with the IRB, as well as the sponsor and CRO.

Ethical conduct

Clinical trials are closely supervised by appropriate regulatory authorities. All studies involving a medical or therapeutic intervention on patients must be approved by a supervising ethics committee before permission is granted to run the trial. The local ethics committee has discretion on how it will supervise noninterventional studies (observational studies or those using already collected data). In the US, this body is called the Institutional Review Board (IRB). Most IRBs are located at the local investigator's hospital or institution, but some sponsors allow the use of a central (independent/for profit) IRB for investigators who work at smaller institutions.
To be ethical, researchers must obtain the full and informed consent of participating human subjects. (One of the IRB's main functions is to ensure potential patients are adequately informed about the clinical trial.) If the patient is unable to consent for him/herself, researchers can seek consent from the patient's legally authorized representative. In California, the state has prioritized the individuals who can serve as the legally authorized representative.[23]
In some US locations, the local IRB must certify researchers and their staff before they can conduct clinical trials. They must understand the federal patient privacy (HIPAA) law and good clinical practice. The International Conference of Harmonisation Guidelines for Good Clinical Practice is a set of standards used internationally for the conduct of clinical trials. The guidelines aim to ensure the "rights, safety and well being of trial subjects are protected".
The notion of informed consent of participating human subjects exists in many countries all over the world, but its precise definition may still vary.
Informed consent is clearly a 'necessary' condition for ethical conduct but does not 'ensure' ethical conduct. The final objective is to serve the community of patients or future patients in a best-possible and most responsible way. However, it may be hard to turn this objective into a well-defined, quantified, objective function. In some cases this can be done, however, for instance, for questions of when to stop sequential treatments (see Odds algorithm), and then quantified methods may play an important role.
Additional ethical concerns are present when conducting clinical trials on children (pediatrics).

Commercial ties and unfavorable studies

Due to repeated accusations and findings that some clinical trials conducted or funded by pharmaceutical companies may report only positive results for the preferred medication, the industry has been looked at much more closely by independent groups and government agencies.[24]
In response to specific cases in which unfavorable data from pharmaceutical company-sponsored research was not published, the Pharmaceutical Research and Manufacturers of America have published new guidelines urging companies to report all findings and limit the financial involvement in drug companies of researchers.[25] US congress signed into law a bill which requires phase II and phase III clinical trials to be registered by the sponsor on the clinical trials website run by the NIH.[26]
Drug researchers not directly employed by pharmaceutical companies often look to companies for grants, and companies often look to researchers for studies that will make their products look favorable. Sponsored researchers are rewarded by drug companies, for example with support for their conference/symposium costs. Lecture scripts and even journal articles presented by academic researchers may actually be 'ghost-written' by pharmaceutical companies.[27] Some researchers who have tried to reveal ethical issues with clinical trials or who tried to publish papers that show harmful effects of new drugs or cheaper alternatives have been threatened by drug companies with lawsuits.[28][29]

Safety

Responsibility for the safety of the subjects in a clinical trial is shared between the sponsor, the local site investigators (if different from the sponsor), the various IRBs that supervise the study, and (in some cases, if the study involves a marketable drug or device), the regulatory agency for the country where the drug or device will be sold.
For safety reasons, many clinical trials of drugs are designed to exclude women of childbearing age, pregnant women, and/or women who become pregnant during the study. In some cases, the male partners of these women are also excluded or required to take birth control measures.

Sponsor

Throughout the clinical trial, the sponsor is responsible for accurately informing the local site investigators of the true historical safety record of the drug, device or other medical treatments to be tested, and of any potential interactions of the study treatment(s) with already approved medical treatments. This allows the local investigators to make an informed judgment on whether to participate in the study or not. The sponsor is also responsible for monitoring the results of the study as they come in from the various sites, as the trial proceeds. In larger clinical trials, a sponsor will use the services of a data monitoring committee (DMC, known in the US as a data safety monitoring board). This independent group of clinicians and statisticians meets periodically to review the unblinded data the sponsor has received so far. The DMC has the power to recommend termination of the study based on their review, for example if the study treatment is causing more deaths than the standard treatment, or seems to be causing unexpected and study-related serious adverse events.The sponsor is responsible for collecting adverse event reports from all site investigators in the study, and for informing all the investigators of the sponsor's judgment as to whether these adverse events were related or not related to the study treatment. This is an area where sponsors can slant their judgment to favor the study treatment.
The sponsor and the local site investigators are jointly responsible for writing a site-specific informed consent that accurately informs the potential subjects of the true risks and potential benefits of participating in the study, while at the same time presenting the material as briefly as possible and in ordinary language. FDA regulations and ICH guidelines both require "the information that is given to the subject or the representative shall be in language understandable to the subject or the representative." If the participant's native language is not English, the sponsor must translate the informed consent into the language of the participant.[30]

Local site investigators

A physician's first duty is to his/her patients, and if a physician investigator believes the study treatment may be harming subjects in the study, the investigator can stop participating at any time. On the other hand, investigators often have a financial interest in recruiting subjects, and can act unethically to obtain and maintain their participation.
The local investigators are responsible for conducting the study according to the study protocol, and supervising the study staff throughout the duration of the study. The local investigator or his/her study staff are also responsible for ensuring the potential subjects in the study understand the risks and potential benefits of participating in the study; in other words, they (or their legally authorized representatives) must give truly informed consent. They are responsible for reviewing all adverse event reports sent by the sponsor. (These adverse event reports contain the opinion of both the investigator at the site where the adverse event occurred, and the sponsor, regarding the relationship of the adverse event to the study treatments). They also are responsible for making an independent judgment of these reports, and promptly informing the local IRB of all serious and study treatment-related adverse events.
When a local investigator is the sponsor, there may not be formal adverse event reports, but study staff at all locations are responsible for informing the coordinating investigator of anything unexpected. The local investigator is responsible for being truthful to the local IRB in all communications relating to the study.

Institutional review boards (IRBs)

Approval by an Institutional Review Board (IRB), or ethics board, is necessary before all but the most informal medical research can begin. In commercial clinical trials, the study protocol is not approved by an IRB before the sponsor recruits sites to conduct the trial. However, the study protocol and procedures have been tailored to fit generic IRB submission requirements. In this case, and where there is no independent sponsor, each local site investigator submits the study protocol, the consent(s), the data collection forms, and supporting documentation to the local IRB. Universities and most hospitals have in-house IRBs. Other researchers (such as in walk-in clinics) use independent IRBs.
The IRB scrutinizes the study for both medical safety and protection of the patients involved in the study, before it allows the researcher to begin the study. It may require changes in study procedures or in the explanations given to the patient. A required yearly "continuing review" report from the investigator updates the IRB on the progress of the study and any new safety information related to the study.

Regulatory agencies

If a clinical trial concerns a new regulated drug or medical device (or an existing drug for a new purpose), the appropriate regulatory agency for each country where the sponsor wishes to sell the drug or device is supposed to review all study data before allowing the drug/device to proceed to the next phase, or to be marketed. However, if the sponsor withholds negative data, or misrepresents data it has acquired from clinical trials, the regulatory agency may make the wrong decision. However, if leaders of the regulatory agency are friendly to industry, they may pressure staff scientists to make decisions favorable to industry, disregard their findings, or make it otherwise difficult for them to do their job.[31]
In the US, the FDA can audit the files of local site investigators after they have finished participating in a study, to see if they were correctly following study procedures. This audit may be random, or for cause (because the investigator is suspected of fraudulent data). Avoiding an audit is an incentive for investigators to follow study procedures.
Alternatively, many American pharmaceutical companies have moved their clinical trials overseas. By doing this they are able to avoid many of the FDA’s regulations, since the FDA rarely investigates clinical trials outside the United States. This increases the ability of pharmaceutical companies to skew results obtained overseas in order to make a profit. [32]
Different countries have different regulatory requirements and enforcement abilities. An estimated 40% of all clinical trials now take place in Asia, Eastern Europe, and Central and South America. "There is no compulsory registration system for clinical trials in these countries and many do not follow European directives in their operations", says Dr. Jacob Sijtsma of the Netherlands-based WEMOS, an advocacy health organisation tracking clinical trials in developing countries.[33]
Beginning in the 1980s, harmonization of clinical trial protocols was shown as feasible across countries of the European Union. At the same time, coordination between Europe, Japan and the United States led to a joint regulatory-industry initiative on international harmonization named after 1990 as the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH)[34] Currently, most clinical trial programs follow ICH guidelines, aimed at "ensuring that good quality, safe and effective medicines are developed and registered in the most efficient and cost-effective manner. These activities are pursued in the interest of the consumer and public health, to prevent unnecessary duplication of clinical trials in humans and to minimize the use of animal testing without compromising the regulatory obligations of safety and effectiveness."[35]



Medical Informatics

Medical informatics is the multidisciplinary field that combines healthcare, computer science, and information technology to optimize the collection, storage, analysis, and use of patient data. Its core purpose is to enhance clinical decision-making, improve patient outcomes, and streamline medical research. 
Key Components
  • Clinical Informatics: Focuses on electronic health records (EHRs), computerized provider order entry, and clinical decision support tools at the point of care.
  • Bioinformatics: Applies computational tools to manage and analyze biological data, such as genomic and proteomic sequences.
  • Public Health Informatics: Manages population data, disease surveillance systems, and community health interventions.
  • Consumer Health Informatics: Empowers patients to access, understand, and manage their own health information using digital tools. 
Primary Benefits
  • Better Safety: Reduces medical and medication errors through automated alerts and safety checks.
  • Improved Access: Streamlines communication and data sharing between doctors, nurses, laboratories, and patients.
  • Informed Decisions: Uses aggregated data and artificial intelligence trends to guide diagnostics and treatment paths. 

Tuesday, 28 July 2026

The difference between genetics and genomics

Genetics and genomics both study DNA, with genetics focusing on individual genes and heredity, while genomics addresses the entire genome and gene interactions. You can read more about these definitions through the National Human Genome Research Institute
Key Differences
  • Scope: Genetics studies single genes or specific groups of genes; genomics studies all genes combined (the genome) and non-coding DNA. 
  • Focus: Genetics looks at how traits and single-gene diseases are inherited; genomics looks at complex multi-gene interactions and environmental influences. 
  • Methods: Genetics uses family history and targeted gene tests; genomics uses high-performance computing, mapping, and large-scale sequencing. 

Medical genomics

Medical genomics is the study and use of all genes in a person's DNA to prevent, find, and treat health problems. It helps doctors choose the best care for your body type, which is also called genomic medicine or precision medicine
Key Uses in Healthcare
  • Finding Diseases: Spotting gene changes that cause rare sicknesses or child disorders.
  • Cancer Care: Testing tumor DNA to find the right targeted therapy.
  • Personalized Medicine: Predicting how a patient might react to specific drugs. 

Medicometrics

Medicometrics is a specialized branch of informatics and data science that applies mathematical, statistical, and quantitative methods to analyze medical literature, research outputs, and health data. It acts as a bridge between medicine and data analytics, serving as a framework to evaluate everything from individual clinical trials to broad healthcare systems. 

Core Areas of Medicometrics

Because it is a broad term, medicometrics functions across three distinct dimensions of healthcare data:
  • Scientometrics of Medicine: It measures the "output" of medical science. This includes tracking research publication trends, analyzing the collaboration networks of medical scientists, and evaluating citation impacts within specific medical fields.
  • Clinical and Operational Metrics: It tracks quantitative markers within patient care, such as hospital readmission rates, clinical treatment outcomes, and resource allocation efficiency.
  • Biomedical Sub-disciplines: It encompasses specific technical modeling fields, such as Pharmacometrics (using mathematical equations to model how drugs interact with human populations) and healthcare psychometrics. 
Related Quantitative Disciplines

Medicometrics belongs to a family of quantitative evaluation sciences that use the suffix -metrics to mean "the science of measuring": 
Field Focus Area
Medicometrics Medical literature, clinical output, and health systems data.
Biometrics Statistical analysis of biological observations and physical human traits.
Scientometrics The quantitative study of scientific publications and academic research as a whole.
Pharmacometrics Mathematical and statistical modeling of drug-patient interactions.


 

Chemometrics

Chemometrics is the science of extracting information from chemical systems by data-driven means. It is a highly interfacial discipline, using methods frequently employed in core data-analytic disciplines such as multivariate statistics, applied mathematics, and computer science, in order to address problems in chemistry, biochemistry, medicine, biology and chemical engineering. In this way, it mirrors several other interfacial ‘-metrics’ such as psychometrics and econometrics.

 Introduction
Chemometrics is applied to solve both descriptive and predictive problems in experimental life sciences, especially in chemistry. In descriptive applications, properties of chemical systems are modeled with the intent of learning the underlying relationships and structure of the system (i.e., model understanding and identification). In predictive applications, properties of chemical systems are modeled with the intent of predicting new properties or behavior of interest. In both cases, the datasets can be small but are often very large and highly complex, involving hundreds to thousands of variables, and hundreds to thousands of cases or observations.
Chemometric techniques are particularly heavily used in analytical chemistry and metabolomics, and the development of improved chemometric methods of analysis also continues to advance the state of the art in analytical instrumentation and methodology. It is an application driven discipline, and thus while the standard chemometric methodologies are very widely used industrially, academic groups are dedicated to the continued development of chemometric theory, method and application development.

Origins

Although one could argue that even the earliest analytical experiments in chemistry involved a form of chemometrics, the field is generally recognized to have emerged in the 1970s as computers became increasingly exploited for scientific investigation. The term ‘chemometrics’ was coined by Svante Wold in a grant application 1971,[1] and the International Chemometrics Society was formed shortly thereafter by Svante Wold and Bruce Kowalski, two pioneers in the field. Wold was a professor of organic chemistry at Umeå University, Sweden, and Kowalski was a professor of analytical chemistry at University of Washington, Seattle.
Many early applications involved multivariate classification, numerous quantitative predictive applications followed, and by the late 1970s and early 1980s a wide variety of data- and computer-driven chemical analyses were occurring.
Multivariate analysis was a critical facet even in the earliest applications of chemometrics. The data resulting from infrared and UV/visible spectroscopy are often easily numbering in the thousands of measurements per sample. Mass spectrometry, nuclear magnetic resonance, atomic emission/absorption and chromatography experiments are also all by nature highly multivariate. The structure of these data was found to be conducive to using techniques such as principal components analysis (PCA), and partial least-squares (PLS). This is primarily because, while the datasets may be highly multivariate there is strong and often linear low-rank structure present. PCA and PLS have been shown over time very effective at empirically modeling the more chemically interesting low-rank structure, exploiting the interrelationships or ‘latent variables’ in the data, and providing alternative compact coordinate systems for further numerical analysis such as regression, clustering, and pattern recognition. Partial least squares in particular was heavily used in chemometric applications for many years before it began to find regular use in other fields.
Through the 1980s three dedicated journals appeared in the field: Journal of Chemometrics, Chemometrics and Intelligent Laboratory Systems, and Journal of Chemical Information and Modeling. These journals continue to cover both fundamental and methodological research in chemometrics. At present, most routine applications of existing chemometric methods are commonly published in application-oriented journals (e.g., Applied Spectroscopy, Analytical Chemistry, Anal. Chim. Acta., Talanta). Several important books/monographs on chemometrics were also first published in the 1980s, including the first edition of Malinowski’s "Factor Analysis in Chemistry",[2] Sharaf, Illman and Kowalski’s "Chemometrics",[3] Massart et al. "Chemometrics: a textbook",[4] and "Multivariate Calibration" by Martens and Naes.[5]
Some large chemometric application areas have gone on to represent new domains, such as molecular modeling and QSAR, cheminformatics, the ‘-omics’ fields of genomics, proteomics, metabonomics and metabolomics, process modeling and process analytical technology.
An account of the early history of chemometrics was published as a series of interviews by Geladi and Esbensen.[6][7]

Techniques

Multivariate calibration

Many chemical problems and applications of chemometrics involve calibration. The objective is develop models which can be used to predict properties of interest based on measured properties of the chemical system, such as pressure, flow, temperature, infrared, Raman, NMR spectra and mass spectra. Examples include the development of multivariate models relating 1) multi-wavelength spectral response to analyte concentration, 2) molecular descriptors to biological activity, 3) multivariate process conditions/states to final product attributes. The process requires a calibration or training data set, which includes reference values for the properties of interest for prediction, and the measured attributes believed to correspond to these properties. For case 1), for example, one can assemble data from a number of samples, including concentrations for an analyte of interest for each sample (the reference) and the corresponding infrared spectrum of that sample. Multivariate calibration techniques such as partial-least squares regression, or principal component regression (and near countless other methods) are then used to construct a mathematical model that relates the multivariate response (spectrum) to the concentration of the analyte of interest, and such a model can be used to efficiently predict the concentrations of new samples.
Techniques in multivariate calibration are often broadly categorized as classical or inverse methods.[5][8] The principal difference between these approaches is that in classical calibration the models are solved such that they are optimal in describing the measured analytical responses (e.g., spectra) and can therefore be considered optimal descriptors, whereas in inverse methods the models are solved to be optimal in predicting the properties of interest (e.g., concentrations, optimal predictors).[9] Inverse methods usually require less physical knowledge of the chemical system, and at least in theory provide superior predictions in the mean-squared error sense,[10][11][12] and hence inverse approaches tend to be more frequently applied in contemporary multivariate calibration.
The main advantages of the use of multivariate calibration techniques is that fast, cheap, or non-destructive analytical measurements (such as optical spectroscopy) can be used to estimate sample properties which would otherwise require time-consuming, expensive or destructive testing (such as HPLC-MS). Equally important is that multivariate calibration allows for accurate quantitative analysis in the presence of heavy interference by other analytes. The selectivity of the analytical method is provided as much by the mathematical calibration, as the analytical measurement modalities. For example near-infrared spectra, which are extremely broad and non-selective compared to other analytical techniques (such as infrared or Raman spectra), can often be used successfully in conjunction with carefully developed multivariate calibration methods to predict concentrations of analytes in very complex matrices.

Classification, pattern recognition, clustering

Supervised multivariate classification techniques are closely related to multivariate calibration techniques in that a calibration or training set is used to develop a mathematical model capable of classifying future samples. The techniques employed in chemometrics are similar to those used in other fields – multivariate discriminant analysis, logistic regression, neural networks, regression/classification trees. The use of rank reduction techniques in conjunction with these conventional classification methods is routine in chemometrics, for example discriminant analysis on principal components or partial least squares scores.
Unsupervised classification (also termed cluster analysis) is also commonly used to discover patterns in complex data sets, and again many of the core techniques used in chemometrics are common to other fields such as machine learning and statistical learning.

Multivariate curve resolution

In chemometric parlance, multivariate curve resolution seeks to deconstruct data sets with limited or absent reference information and system knowledge. Some of the earliest work on these techniques was done by Lawton and Sylvestre in the early 1970s.[13][14] These approaches are also called self-modeling mixture analysis, blind source/signal separation, and spectral unmixing. For example, from a data set comprising fluorescence spectra from a series of samples each containing multiple fluorophores, multivariate curve resolution methods can be used to extract the fluorescence spectra of the individual fluorophores, along with their relative concentrations in each of the samples, essentially unmixing the total fluorescence spectrum into the contributions from the individual components. The problem is usually ill-determined due to rotational ambiguity (many possible solutions can equivalently represent the measured data), so the application of additional constraints is common, such as non-negatively, unmodality, or known interrelationships between the individual components (e.g., kinetic or mass-balance constraints).[15][16]

Other techniques

Experimental design remains a core area of study in chemometrics and several monographs are specifically devoted to experimental design in chemical applications.[17][18] Sound principles of experimental design have been widely adopted within the chemometrics community, although many complex experiments are purely observational, and there can be little control over the properties and interrelationships of the samples and sample properties.
Signal processing is also a critical component of almost all chemometric applications, particularly the use of signal pretreatments to condition data prior to calibration or classification. The techniques employed commonly in chemometrics are often closely related to those used in related fields.[19]
Performance characterization, and figures of merit Like most arenas in the physical sciences, chemometrics is quantitatively oriented, so considerable emphasis is placed on performance characterization, model selection, verification & validation, and figures of merit. The performance of quantitative models is usually specified by root mean squared error in predicting the attribute of interest, and the performance of classifiers as a true-positive rate/false-positive rate pairs (or a full ROC curve). A recent report by Olivieri et al. provides a comprehensive overview of figures of merit and uncertainty estimation in multivariate calibration, including multivariate definitions of selectivity, sensitivity, SNR and prediction interval estimation.[20] Chemometric model selection usually involves the use of tools such as resampling (including bootstrap, permutation, cross-validation).
Multivariate statistical process control (MSPC), modeling and optimization accounts for a substantial amount of historical chemometric development.[21][22][23] Spectroscopy has been used successfully for online monitoring of manufacturing processes for 30–40 years, and this process data is highly amenable to chemometric modeling. Specifically in terms of MSPC, multiway modeling of batch and continuous processes is increasingly common in industry and remains an active area of research in chemometrics and chemical engineering. Process analytical chemistry as it was originally termed,[24] or the newer term process analytical technology continues to draw heavily on chemometric methods and MSPC.
Multiway methods are heavily used in chemometric applications.[25][26] These are higher-order extensions of more widely used methods. For example, while the analysis of a table (matrix, or second-order array) of data is routine in several fields, multiway methods are applied to data sets that involve 3rd, 4th, or higher-orders. Data of this type is very common in chemistry, for example a liquid-chromatography / mass spectrometry (LC-MS) system generates a large matrix of data (elution time versus m/z) for each sample analyzed. The data across multiple samples thus comprises a data cube. Batch process modeling involves data sets that have time vs. process variables vs. batch number. The multiway mathematical methods applied to these sorts of problems include PARAFAC, trilinear decomposition, and multiway PLS and PCA.



Chemometrics is the science of extracting useful information from chemical systems by applying mathematical, statistical, and computer science methods. It helps design optimal experiments and analyzes large, complex multivariate data generated by analytical chemistry tools like spectroscopy and chromatography. 
Key Goals of Chemometrics
  • Data Extraction: Turning raw, noisy instrument signals into clear chemical insights.
  • Experimental Design: Planning optimal experiments and sampling strategies to reduce error and save time.
  • Prediction & Modeling: Building mathematical models to forecast properties, concentrations, or behavior of new samples. 
Common Techniques Used
  • Multivariate Analysis: Methods like Principal Component Analysis (PCA) to find hidden patterns and group similar samples.
  • Calibration: Connecting instrument readouts (like light absorption) directly to chemical concentrations.
  • Pattern Recognition: Classifying unknown substances or identifying quality control traits in manufacturing and food processing. 
If you'd like, let me know:
  • What type of data or instrument you are working with (e.g., NIR, HPLC, mass spectrometry)