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Characterization and Formulation programs at The Bioprocessing Summit

Dear Colleague -This year’s Characterization and Formulation portion of CHI’s 5th Annual Bioprocessing Summit will consist of two conferences and two workshops taking place Monday-Thursday (August 19-22, 2013) in Boston.

The two conferences are Higher-Order Protein Structure: Characterization and Prediction and High-Concentration Protein Formulations: Overcoming Challenges in Stability and Aggregation.

The workshops on Monday morning focuses on Sub-Visible Particle Analysis in High-Concentration and Viral Protein Formulations and Strategies for Development of Analytical Specifications. The dinner workshop on Tuesday will delve into Accelerated Stability Testing of Biologics. Attendees and speakers are encouraged to participate in all sessions throughout the week.

Please look at the detailed agendas online and the Conference-at-a-Glance below. Register before May 31 to save up to $400 off the standard rates!

Higher-Order Protein Structure: Characterization and PredictionBioprocessingSummit.com/Protein-Structure

&

High-Concentration Protein Formulations: Overcoming Challenges in Stability and Aggregation

BioprocessingSummit.com/Protein-Formulations

August 19-22, 2013 * Renaissance Waterfront Hotel * Boston, MA

Conference-at-a-Glance

Mon., August 19 Tues., August 20 Wed., August 21 Thurs., August 22
AM SessionPre-Conference Workshops:

– Sub-Visible Particle Analysis in High-Concentration and Viral Protein
– Strategies for Development of Analytical Specifications

AM SessionHigher-Order Protein Structure AM SessionHigh-Concentration Protein Formulations AM SessionHigh-Concentration Protein Formulations
Lunch Lunch Lunch Lunch
PM SessionHigher-Order Protein Structure PM SessionHigher-Order Protein Structure PM SessionHigh-Concentration Protein Formulations
Breakout Discussion Groups
Grand Opening Reception with Exhibit and Poster Viewing Dinner Workshop:Accelerated Stability Testing of Biologics Networking Reception with Exhibit and Poster Viewing

Get updates on the meeting and connect with experts by joining our LinkedIn Group.

I look forward to seeing you in Boston!

Best regards,

Nandini Kashyap
Conference Director
Cambridge Healthtech Institute

PS:For partnering and sponsorship information, please contact:
Jason Gerardi
Manager, Business Development
Cambridge Healthtech Institute (CHI)
Phone: 781-972-5452
E: jgerardi

Cambridge Healthtech Institute, 250 First Avenue, Suite 300, Needham, MA 02494 | healthtech.com

 

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HEALTHbeat Harvard Medical School
May 23, 2013
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Living Well with Osteoarthritis

If you think you might need a hearing checkup, you probably do. This Special Health Report describes the causes and cures for hearing loss. It contains in-depth information on the causes, diagnosis, and treatment of hearing loss. You’ll learn how to prevent hearing loss and preserve the hearing you have now. You’ll also learn about the latest advances in hearing aid technology and find out which kind of hearing device may be best for you.

Read More

Testing for hearing loss

The human ear is the envy of even the most sophisticated acoustic engineer. Without a moment’s thought or the slightest pause, you can hear the difference between a violin and a clarinet, you can tell if a sound is coming from your left or right, and if it’s distant or near. And you can discriminate between words as similar as hear and near, sound and pound.

Nearly everyone experiences trouble hearing from time to time. Common causes include a buildup of ear wax or fluid in the ear, ear infections, or the change in air pressure when taking off in an airplane. A mild degree of permanent hearing loss is an inevitable part of the aging process. Unfortunately, major hearing loss that makes communication difficult becomes more common with increasing age, particularly after age 65.

Testing, 1, 2, 3

How do you know if you need a hearing test? If you answer yes to the questions below, talk with your doctor about having your hearing tested:

Are you always turning up the volume on your TV or radio?
Do you shy away from social situations or meeting new people because you’re worried about understanding them?
Do you get confused or feel “out of it” at restaurants or dinner parties?
Do you ask people to repeat themselves?
Do you miss telephone calls — or have trouble hearing on the phone when you do pick up the receiver?
Do the people in your world complain that you never listen to them (even when you’re really trying)?

You can also ask a friend to test you by whispering a series of words or numbers. After all this, if you think you have a hearing problem, you should have a test.

For more on diagnosing and treating hearing loss, buy Hearing Loss, a Special Health Report from Harvard Medical School.

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News and Views from the Harvard Health Blog

A dog could be your heart’s best friend

Is having a dog good for you heart? Yes, says the American Heart Association — as long as you don’t delegate the dog walking responsibilities to others.Read more.

7 tips for buying a hearing aid

With so many types of hearing aid on the market, which one is right for you? The answer depends on many things. The main consideration is the nature of your hearing loss, its cause, and its severity. The results of your hearing tests will guide your audiologist or hearing aide specialist in making recommendations. Here are seven things you should know as you evaluate your options.

1. If you have severe hearing loss, you may need one of the larger hearing aids.
2. If you are prone to an excessive buildup of earwax or to ear infections, small hearing aids are easily damaged by earwax or draining ear fluid and so may not be the best choice for you.
3. You may want to be able to reduce some types of background noise and boost the sound frequencies you have the most trouble hearing — something not all small hearing aids can do.
4. If you use electronic devices like cell phones, music players, or laptops that are capable of sending a wireless signal, then you may want a hearing aid that is compatible with wireless devices that are important to you.
5. If you are concerned about how you’ll look wearing a hearing aid, let your audiologist know. She or he can help narrow the choices to what will best suit both your hearing needs and your appearance.
6. Hearing aids range in price from about $1,200 to $3,700 each, depending on size and features. Unfortunately, Medicare and most other insurance plans don’t cover hearing aids, so your budget may be a factor in your decision.
7. Finally, consider your dexterity. If you have arthritis, you may find it difficult to insert and remove the smallest hearing aid, and gladly opt for a larger one that’s easier to handle.

For additional advice on diagnosing hearing loss as well as the best ways to treat it, buy Hearing Loss, a Special Health Report from Harvard Medical School.

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Hearing Loss

Featured content:

How we hear
When hearing loss occurs
Testing for hearing loss
Progress in hearing technology
… and more!

Click here to read more »

Harvard Medical School offers special reports on over 50 health topics. Visit our website at to find reports of interest to you and your family.

Copyright © 2013 by Harvard University.

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Clinical Study Design and Methods Terminology


Clinical Epidemiology & Evidence-Based Medicine Glossary:

Clinical Study Design and Methods Terminology

Updated November 02, 2010

Contents:


  1. Clinical Study Types: (In order from strongest to weakest empirical evidence inherent to the design when properly executed.)
    1. Experimental Studies: The hallmark of the experimental study is that the allocation or assignment of individuals is under control of investigator and thus can be randomized. The key is that the investigator controls the assignment of the exposure or of the treatment but otherwise symmetry of potential unknown confounders is maintained through randomization. Properly executed experimental studies provide the strongest empirical evidence. The randomization also provides a better foundation for statistical procedures than do observational studies.
      1. Randomized Controlled Clinical Trial (RCT): A prospective, analytical, experimental study using primary data generated in the clinical environment. Individuals similar at the beginning are randomly allocated to two or more treatment groups and the outcomes the groups are compared after sufficient follow-up time. Properly executed, the RCT is the strongest evidence of the clinical efficacy of preventive and therapeutic procedures in the clinical setting.
      2. Randomized Cross-Over Clinical Trial: A prospective, analytical, experimental study using primary data generated in the clinical environment. Individuals with a chronic condition are randomly allocated to one of two treatment groups, and, after a sufficient treatment period and often a washout period, are switched to the other treatment for the same period. This design is susceptible to bias if carry over effects from the first treatment occur. An important variant is the “N of One” clinical trial in which alternative treatments for a chronically affected individual are administered in a random sequence and the individual is observed in a double blind fashion to determine which treatment is the best.
      3. Randomized Controlled Laboratory Study: A prospective, analytical, experimental study using primary data generated in the laboratory environment. Laboratory studies are very powerful tools for doing basic research because all extraneous factors other than those of interest can be controlled or accounted for (e.g., age, gender, genetics, nutrition, environment, co-morbidity, strain of infectious agent). However, this control of other factors is also the weakness of this type of study. Animals in the clinical environment have a wide range of all these controlled factors as well as others that are unknown. If any interactions occur between these factors and the outcome of interest, which is usually the case, the laboratory results are not directly applicable to the clinical setting unless the impact of these interactions are also investigated.
    2. Observational Studies: The allocation or assignment of factors is not under control of investigator. In an observational study, the combinations are self-selected or are “experiments of nature”. For those questions where it would be unethical to assign factors, investigators are limited to observational studies. Observational studies provide weaker empirical evidence than do experimental studies because of the potential for large confounding biases to be present when there is an unknown association between a factor and an outcome. The symmetry of unknown confounders cannot be maintained. The greatest value of these types of studies (e.g., case series, ecologic, case-control, cohort) is that they provide preliminary evidence that can be used as the basis for hypotheses in stronger experimental studies, such as randomized controlled trials.
      1. Cohort (Incidence, Longitudinal Study) Study: A prospective, analytical, observational study, based on data, usually primary, from a follow-up period of a group in which some have had, have or will have the exposure of interest, to determine the association between that exposure and an outcome. Cohort studies are susceptible to bias by differential loss to follow-up, the lack of control over risk assignment and thus confounder symmetry, and the potential for zero time bias when the cohort is assembled. Because of their prospective nature, cohort studies are stronger than case-control studies when well executed but they also are more expensive. Because of their observational nature, cohort studies do not provide empirical evidence that is as strong as that provided by properly executed randomized controlled clinical trials.
      2. Case-Control Study: A retrospective, analytical, observational study often based on secondary data in which the proportion of cases with a potential risk factor are compared to the proportion of controls (individuals without the disease) with the same risk factor. The common association measure for a case-control study is the odds ratio. These studies are commonly used for initial, inexpensive evaluation of risk factors and are particularly useful for rare conditions or for risk factors with long induction periods. Unfortunately, due to the potential for many forms of bias in this study type, case control studies provide relatively weak empirical evidence even when properly executed.
      3. Ecologic (Aggregate) Study: An observational analytical study based on aggregated secondary data. Aggregate data on risk factors and disease prevalence from different population groups is compared to identify associations. Because all data are aggregate at the group level, relationships at the individual level cannot be empirically determined but are rather inferred from the group level. Thus, because of the likelihood of an ecologic fallacy, this type of study provides weak empirical evidence.
      4. Cross-Sectional (Prevalence Study) Study: A descriptive study of the relationship between diseases and other factors at one point in time (usually) in a defined population. Cross sectional studies lack any information on timing of exposure and outcome relationships and include only prevalent cases.
      5. Case Series: A descriptive, observational study of a series of cases, typically describing the manifestations, clinical course, and prognosis of a condition. A case series provides weak empirical evidence because of the lack of comparability unless the findings are dramatically different from expectations. Case series are best used as a source of hypotheses for investigation by stronger study designs, leading some to suggest that the case series should be regarded as clinicians talking to researchers. Unfortunately, the case series is the most common study type in the clinical literature.
      6. Case Report: Anecdotal evidence. A description of a single case, typically describing the manifestations, clinical course, and prognosis of that case. Due to the wide range of natural biologic variability in these aspects, a single case report provides little empirical evidence to the clinician. They do describe how others diagnosed and treated the condition and what the clinical outcome was.
  1. Validity vs. Bias:
    1. Validity: Truth
      1. External Validity (Generalizability): Truth beyond a study. A study is external valid if the study conclusions represent the truth for the population to which the results will be applied because both the study population and the reader’s population are similar enough in important characteristics. The important characteristics are those that would be expected to have an impact on a study’s results if they were different (e.g., age, previous disease history, disease severity, nutritional status, co-morbidity, …). Whether or not the study is generalizable to the population of interest to the reader is a question only the reader can answer. External validity can occur only if the study is first internally valid.
      2. Internal Validity: Truth within a study. A study is internally valid if the study conclusions represent the truth for the individuals studied because the results were not likely due to the effects of chance, bias, or confounding because the study design, execution, and analysis were correct. The statistical assessment of the effects of chance is meaningless if sufficient bias has occurred to invalidate the study. All studies are flawed to some degree. The crucial question that the reader must answer is whether or not these problems were great enough that the study results are more likely due to the flaws than the hypothesis under investigation.
      3. Symmetry Principle: In a study, the principle of keeping all things between groups similar except for the treatment of interest. This means that the same instrument is used to measure each individual in each group, the observers know the same things about all individuals in all groups, randomization is used to obtain a similar allocation of individuals to each group, the groups are followed at the same time, … .
  1. Confounding: Confounding is the distortion of the effect of one risk factor by the presence of another. Confounding occurs when another risk factor for a disease is also associated with the risk factor being studied but acts separately. Age, breed, gender and production levels are often confounding risk factors because animals with different values of these are often at different risk of disease. As a result of the association between the study and confounding risk factor, the confounder is not distributed randomly between the group with the study risk factor and the control group without the study factor. Confounding can be controlled by restriction, by matching on the confounding variable or by including it in the statistical analysis.
  1. Bias (Systematic Error): Any process or effect at any stage of a study from its design to its execution to the application of information from the study, that produces results or conclusions that differsystematically from the truth. Bias can be reduced only by proper study design and execution and not by increasing sample size (which only increases precision by reducing the opportunity for random chance deviation from the truth). Almost all studies have bias, but to varying degrees. The critical question is whether or not the results could be due in large part to bias, thus making the conclusions invalid. Observational study designs are inherently more susceptible to bias than are experimental study designs.
    1. Confounding Bias: Systematic error due to the failure to account for the effect of one or more variables that are related to both the causal factor being studied and the outcome and are not distributed the same between the groups being studied. The different distribution of these “lurking” variables between groups alters the apparent relationship between the factor of interest and the outcome. Confounding can be accounted for if the confounding variables are measured and are included in the statistical models of the cause-effect relationships.
    2. Ecological (Aggregation) Bias (Fallacy): Systematic error that occurs when an association observed between variables representing group averages is mistakenly taken to represent the actual association that exists between these variables for individuals. This bias occurs when the nature of the association at the individual level is different from the association observed at the group level. Data aggregated from individuals (e.g. census averages for a region) or proxy data from other sources (e.g., the amount of alcohol distributed in a region is a proxy for the amount of alcohol by individuals in that region) are often easier and less expensive to acquire than are data directly from individuals.
    3. Measurement Bias: Systematic error that occurs when, because of the lack of blinding or related reasons such as diagnostic suspicion, the measurement methods (instrument, or observer of instrument) are consistently different between groups in the study.
      1. Screening Bias: The bias that occurs when the presence of a disease is detected earlier during its latent period by screening tests but the course of the disease is not be changed by earlier intervention. Because the survival after screening detection is longer than survival after detection of clinical signs, ineffective interventions appear to be effective unless they are compared appropriately in clinical trials.
    4. Reader Bias: Systematic errors of interpretation made during inference by the user or reader of clinical information (papers, test results, …). Such biases are due to clinical experience, tradition, credentials, prejudice and human nature. The human tendency is to accept information that supports pre-conceived opinions and to reject or trivialize that which does not support preconceived opinions or that which one does not understand. (JAMA 247:2533)
    5. Sampling (Selection) Biases: Systematic error that occurs when, because of design and execution errors in sampling, selection, or allocation methods, the study comparisons are between groups that differ with respect to the outcome of interest for reasons other than those under study.
    6. Zero Time Bias: The bias that occurs in a prospective study when individuals are found and enrolled in such a fashion that unintended systematic differences occur between groups at the beginning of the study (stage of disease, confounder distribution). Cohort studies are susceptible to zero-time bias if the cohort is not assembled properly.
  2. Bias Effect:
    1. Non-differential Bias: Opportunities for bias are equivalent in all study groups, which biases the outcome measure of the study toward the null of no difference between the groups.
    2. Differential Bias: Opportunities for bias are different in different study groups, which biases the outcome measure of the study in unknown ways. Case-control studies are highly susceptible to this form of bias between the case and control groups.

  


  1. Study Objective, Direction and Timing:
    1. Analytic (Explanatory) Study: The objective of an analytic study is to make causal inferences about the nature of hypothesized relationships between risk factors and outcomes. Statistical procedures are used to determine if a relationship was likely to have occurred by chance alone. Analytic studies usually compare two or more groups, such as case-control studies, cohort studies, randomized controlled clinical trials, and laboratory studies.
    2. Descriptive Study: The objective of a descriptive study is to describe the distribution of variables in a group. Statistics serve only to describe the precision of those measurements or to make statistical inferences about the values in the population from which the sample was taken.
    3. Contemporary (Concurrent) Comparison: Comparison is between two groups experiencing the risk factor or the treatment at the same time. Contemporary comparison has the major advantages that symmetry of unknown risk factors for the condition that change over time is maintained and that measurement procedures can be performed as similarly as possible on both groups.
    4. Historical (Non-concurrent) Comparison: Comparison is of the same group or between groups at different times that are not experiencing the risk factor or the treatment at the same time. Historical comparison is often used to allow a group to serve as its own historical control or is done implicitly when a group is compared to expected standards of performance. This design provides weak evidence because symmetry isn’t assured. It is very susceptible to bias by changes over time in uncontrollable, confounding risk factors, such as differences in climate, management practices and nutrition. Bias due to differences in measuring procedures over time may also account for observed differences.
    5. Prospective Study (Data): Data collection and the events of interest occur after individuals are enrolled (e.g. clinical trials and cohort studies). This prospective collection enables the use of more solid, consistent criteria and avoids the potential biases of retrospective recall. Prospective studies are limited to those conditions that occur relatively frequently and to studies with relatively short follow-up periods so that sufficient numbers of eligible individuals can be enrolled and followed within a reasonable period.
    6. Retrospective Study (Data): All events of interest have already occurred and data are generated from historical records (secondary data) and from recall (which may result in the presence of significant recall bias). Retrospective data is relatively inexpensive compared to prospective studies because of the use of available information and is typically used in case-control studies. Retrospective studies of rare conditions are much more efficient than prospective studies because individuals experiencing the rare outcome can be found in patient records rather than following a large number of individuals to find a few cases.

 


  1. Other Terms:
    1. Baseline: Health state (disease severity, confounding conditions) of individuals at beginning of a prospective study. A difference (asymmetry) in the distributions of baseline values between groups will bias the results.
    2. Blinding (Masking): Blinding is those methods to reduce bias by preventing observers and/or experimental subjects involved in an analytic study from knowing the hypothesis being investigated, the case-control classification, the assignment of individuals or groups, or the different treatments being provided. Blinding reduces bias by preserving symmetry in the observers’ measurements and assessments. This bias is usually not due to deliberate deception but is due to human nature and prior held beliefs about the area of study.
      1. Placebo: A placebo is the shame treatment used in a control group in place of the actual treatment. If a drug is being evaluated, the inactive vehicle or carrier is used alone so it is as similar as possible in appearance and in administration to the active drug. Placebos are used to blind observers and, for human trials, the patients to which group the patient is allocated.
    3. Case Definition: The set of history, clinical signs and laboratory findings that are used to classify an individual as a case or not for an epidemiological study. Case definitions are needed to exclude individuals with the other conditions that occur at an endemic background rate in a population or other characteristics that will confuse or reduce the precision of a clinical study.
    4. Cohort: A group of individuals identified on the basis of a common experience or characteristic that is usually monitored over time from the point of assembly.
    5. Experimental Unit, Unit of Concern (EU): In an experiment, the experimental unit are the units that are randomly selected or allocated to a treatment and the unit upon which the sample size calculations and subsequent data analysis must be based. Experimental units are often a pen of animals or a cage of mice rather than the individuals themselves. Analyzing data on an individual basis when groups (herds, pens) have been the basis of random allocation is a serious error because it over-estimates precision, possibly biasing the study toward a false-positive conclusion.

 


  1. Sample Selection / Allocation Procedures:
    1. Matching: When confounding cannot be controlled by randomization, individual cases are matched with individual controls that have similar confounding factors, such as age, to reduce the effect of the confounding factors on the association being investigated in analytic studies. Most commonly seen in case-control studies.
    2. Restriction (Specification): Eligibility for entry into an analytic study is restricted to individuals within a certain range of values for a confounding factor, such as age, to reduce the effect of the confounding factor when it cannot be controlled by randomization. Restriction limits the external validity (generalizability) to those with the same confounder values.
    3. Census: A sample that includes every individual in a population or group (e.g., entire herd, all known cases). A census not feasible when group is large relative to the costs of obtaining information from individuals.
    4. Haphazard, Convenience, Volunteer, Judgmental Sampling: Any sampling not involving a truly random mechanism. A hallmark of this form of sampling is that the probability that a given individual will be in the sample is unknown before sampling;. The theoretical basis for statistical inference is lost and the result is inevitably biased in unknown ways. Despite their best intentions, humans cannot choose a sample in a random fashion without a formal randomizing mechanism.
    5. Consecutive (Quota) Sampling: Sampling individuals with a given characteristic as they are presented until enough with that characteristic are acquired. This method is okay for descriptive studies but unfortunately not much better than haphazard sampling for analytical observational studies.
    6. Random Sampling: Each individual in the group being sampled has a known probability of being included in the sample obtained from the group before the sampling occurs.
    7. Simple Random Sampling / Allocation: Sampling conducted such that each eligible individual in the population has the same chance of being selected or allocated to a group. This sampling procedure is the basis of the simpler statistical analysis procedures applied to sample data. Simple random sampling has the disadvantage of requiring a complete list of identified individuals making up the population (the list frame) before the sampling can be done.
    8. Stratified Random Sampling: The group from which the sample is to be taken is first stratified on the basis of a important characteristic related to the problem at hand (e.g., age, parity, weight) into subgroups such that each individual in a subgroup has the same probability of being included in the sample but the probabilities are different between the subgroups or strata. Stratified random sampling assures that the different categories of the characteristic that is the basis of the strata are sufficiently represented in the sample but the resulting data must be analyzed using more complicated statistical procedures (such as Mantel-Haenszel) in which the stratification is taken into account.
    9. Cluster Sampling: Staged sampling in which a random sample of natural groupings of individuals (houses, herds, kennels, households, stables) are selected and then sampling all the individuals within the cluster. Cluster sampling requires special statistical methods for proper analysis of the data and is not advantageous if the individuals are highly correlated within a group (a strong herd effect).
    10. Systematic Sampling: From a random start in first n individuals, sampling every nth animal as they are presented at the sampling site (clinic, chute, …). Systematic sampling will not produce a random sample if a cyclical pattern is present in the important characteristics of the individuals as they are presented. Systematic sampling has the advantage of requiring only knowledge of the number of animals in the population to establish n and that anyone presenting the animals is blind to the sequence so they cannot bias it.

source:http://www.vetmed.wsu.edu/courses-jmgay/GlossClinStudy.htm#Contents

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Library Information and Management System |General

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LIMS | Qualoupe LIMS Class| 15 Vid’s

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Qualoupe lab info management software is live

LIMS Laboratory information management systems

Two Fold Software partners with Bibby Scientific to provide new Qualoupe Lite software. The software features an intuitive laboratory information management system (LIMS) that helps in automated data management and transfer

UK-based Two Fold Software has partnered with Bibby Scientific, manufacturers of laboratory products, to provide new Qualoupe Lite software for incorporation into Jenway brand 67 series spectrophotometers. Qualoupe Lite provides users with an intuitive laboratory information management system (LIMS) to automate data management and transfer. Using Qualoupe Lite, analysis results and method information can be transferred directly from the spectrophotometers to the database, removing the need for complicated transitional systems. The system is easy-to-use, and requires minimal training to store and recall laboratory data, including the printing of result reports from the sample manager application. “By creating a true Lite product that is affordable for businesses of all sizes, we make LIMS available to companies aiming to move towards automated laboratory data management,” said Mr Clive Collier, managing director, Two Fold Software. “In the past there have been Lite LIMS products available, but the prices rarely made them accessible. This collaboration with Bibby Scientific means more companies can feel the benefit of an intuitive and effective Qualoupe Lite system without committing to large costs.”

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LIMS-Tutorial | Qualoupe LIMS

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Laboratory information management systems

Qualoupe lab info management software is live

Two Fold Software partners with Bibby Scientific to provide new Qualoupe Lite software. The software features an intuitive laboratory information management system (LIMS) that helps in automated data management and transfer

UK-based Two Fold Software has partnered with Bibby Scientific, manufacturers of laboratory products, to provide new Qualoupe Lite software for incorporation into Jenway brand 67 series spectrophotometers. Qualoupe Lite provides users with an intuitive laboratory information management system (LIMS) to automate data management and transfer. Using Qualoupe Lite, analysis results and method information can be transferred directly from the spectrophotometers to the database, removing the need for complicated transitional systems. The system is easy-to-use, and requires minimal training to store and recall laboratory data, including the printing of result reports from the sample manager application. “By creating a true Lite product that is affordable for businesses of all sizes, we make LIMS available to companies aiming to move towards automated laboratory data management,” said Mr Clive Collier, managing director, Two Fold Software. “In the past there have been Lite LIMS products available, but the prices rarely made them accessible. This collaboration with Bibby Scientific means more companies can feel the benefit of an intuitive and effective Qualoupe Lite system without committing to large costs.”

 


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Laboratory Information Management System

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A ”’Laboratory Information Management System”’ (LIMS), sometimes referred to as a ”’Laboratory Information System”’ (LIS) or ”’Laboratory Management System”’ (LMS), is a [[software]]-based [[laboratory]] and information management [[system]]

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that offers a set of key features that support a modern laboratory’s operations. Those key features include — but are not limited to — [[workflow]] and data tracking support, flexible architecture, and smart data exchange interfaces, which fully “support its use in regulated environments.”{{cite web |url=http://sapiosciences.blogspot.com/2010/07/so-what-is-lims.html |title=2011 Laboratory Information Management: So what is a LIMS? |publisher=Sapio Sciences |date=28 July 2010 |accessdate=7 November 2012}} The features and uses of a LIMS have evolved over the years from simple [[Sample (material)|sample]] tracking to an enterprise resource planning tool that manages multiple aspects of [[laboratory informatics]].{{cite web |url=http://www.limsfinder.com/BlogDetail.aspx?id=30648_0_29_0_C |title=LIMS: The Laboratory ERP |author=Vaughan, Alan |publisher=LIMSfinder.com |date=20 August 2006 |accessdate=7 November 2012}}

 

Lims

Due to the rapid pace at which laboratories and their data management needs shift, the definition of LIMS has become somewhat controversial. As the needs of the modern laboratory vary widely from lab to lab, what is needed from a laboratory information management system also shifts. The end result: the definition of a LIMS will shift based on who you ask and what their vision of the modern lab is. Dr. Alan McLelland of the Institute of Biochemistry, Royal Infirmary, Glasgow highlighted this problem in the late 1990s by explaining how a LIMS is perceived by an analyst, a laboratory manager, an information systems manager, and an accountant, “all of them correct, but each of them limited by the users’ own perceptions.”{{cite web |url=http://www.rsc.org/pdf/andiv/tech.pdf |format=PDF |title=What is a LIMS – a laboratory toy, or a critical IT component? |author=McLelland, Alan |publisher=Royal Society of Chemistry |page=1 |year=1998 |accessdate=7 November 2012}}

Historically the LIMS, LIS, and [[Process Development Execution System]] (PDES) have all performed similar functions. Historically the term “LIMS” has tended to be used to reference informatics systems targeted for environmental, research, or commercial analysis such as pharmaceutical or petrochemical work. “LIS” has tended to be used to reference laboratory informatics systems in the forensics and clinical markets, which often required special case management tools. The term “PDES” has generally applied to a wider scope, including, for example, virtual manufacturing techniques, while not necessarily integrating with [[laboratory equipment]].

In recent times LIMS functionality has spread even farther beyond its original purpose of sample management. [[Assay]] data management, [[data mining]], data analysis, and [[electronic laboratory notebook]] (ELN) integration are all features that have been added to many LIMS,{{cite web |url=http://files.limstitute.com/share/lbgprofiles/findlims.pdf |format=PDF |title=How Do I Find the Right LIMS — And How Much Will It Cost? |publisher=Laboratory Informatics Institute, Inc |accessdate=7 November 2012}} enabling the realization of translational medicine completely within a single software solution. Additionally, the distinction between a LIMS and a LIS has blurred, as many LIMS now also fully support comprehensive case-centric clinical data.

==History==
Up until the late 1970s, the management of laboratory samples and the associated analysis and reporting were time-consuming manual processes often riddled with transcription errors. This gave some organizations impetus to streamline the collection of data and how it was reported. Custom in-house solutions were developed by a few individual laboratories, while some enterprising entities at the same time sought to develop a more commercial reporting solution in the form of special instrument-based systems.{{cite journal |journal=Laboratory Automation and Information Management |year=1996 |volume=32 |issue=1 |pages=1–5 |title=A brief history of LIMS |author=Gibbon, G.A.|doi=10.1016/1381-141X(95)00024-K |url=http://www.sciencedirect.com/science/article/pii/1381141X9500024K |format=PDF |accessdate=7 November 2012}}

In 1982 the first generation of LIMS was introduced in the form of a single centralized minicomputer, which offered laboratories the first opportunity to utilize automated reporting tools. As the interest in these early LIMS grew, industry leaders like Gerst Gibbon of the Federal Energy Technology Centre in Pittsburgh began planting the seeds through LIMS-related conferences. By 1988 the second-generation commercial offerings were tapping into [[relational database]]s to expand LIMS into more application-specific territory, and International LIMS Conferences were in full swing. As [[personal computers]] became more powerful and prominent, a third generation of LIMS emerged in the early 1990s. These new LIMS took advantage of the developing client/server architecture, allowing laboratories to implement better data processing and exchanges.

By 1995 the client/server tools had developed to the point of allowing processing of data anywhere on the network. Web-enabled LIMS were introduced the following year, enabling researchers to extend operations outside the confines of the laboratory. From 1996 to 2002 additional functionality was included in LIMS, from [[wireless networking]] capabilities and [[Georeference|georeferencing]] of samples, to the adoption of [[XML]] standards and the development of Internet purchasing.

As of 2012, some LIMS have added additional characteristics that continue to shape how a LIMS is defined. Examples include the addition of clinical functionality, [[electronic laboratory notebook]] (ELN) functionality, as well a rise in the [[software as a service]] (SaaS) distribution model.

==Technology==

===Operations===

The LIMS is an evolving concept, with new features and functionality being added often. As laboratory demands change and technological progress continues, the functions of a LIMS will likely also change. Despite these changes, a LIMS tends to have a base set of functionality that defines it. That functionality can roughly be divided into five laboratory processing phases, with numerous software functions falling under each:{{cite journal|journal=Measurement Techniques |year=2011 |volume=53 |issue=10 |pages=1182–1189 |title=Laboratory information management systems in the work of the analytic laboratory |author=D. O. Skobelev, T. M. Zaytseva, A. D. Kozlov, V. L. Perepelitsa, and A. S. Makarova |doi=10.1007/s11018-011-9638-7 |url=http://www.springerlink.com/content/6564211m773v70j1/ |format=PDF |accessdate=7 November 2012}}

* the reception and log in of a [[Sample (material)|sample]] and its associated customer data
* the assignment, scheduling, and tracking of the sample and the associated analytical workload
* the processing and quality control associated with the sample and the utilized equipment and inventory
* the storage of data associated with the sample analysis
* the inspection, approval, and compilation of the sample data for reporting and/or further analysis

There are several pieces of core functionality associated with these laboratory processing phases that tend to appear in most LIMS:

====Sample management====

[[File:Lab worker with blood samples.jpg|thumb|right|A lab worker matches blood samples to documents. With a LIMS, this sort of sample management is made more efficient.]]
The core function of LIMS has traditionally been the management of samples. This typically is initiated when a sample is received in the laboratory, at which point the sample will be registered in the LIMS. Some LIMS will allow the customer to place an “order” for a sample directly to the LIMS at which point the sample is generated in an “unreceived” state. The processing could then include a step where the sample container is registered and sent to the customer for the sample to be taken and then returned to the lab. The registration process may involve [[accession number (bioinformatics)|accessioning]] the sample and producing [[barcode]]s to affix to the sample container. Various other parameters such as clinical or [[Phenotype|phenotypic]] information corresponding with the sample are also often recorded. The LIMS then tracks chain of custody as well as sample location. Location tracking usually involves assigning the sample to a particular freezer location, often down to the granular level of shelf, rack, box, row, and column. Other event tracking such as freeze and thaw cycles that a sample undergoes in the laboratory may be required.

Modern LIMS have implemented extensive configurability, as each laboratory’s needs for tracking additional data points can vary widely. LIMS vendors cannot typically make assumptions about what these data tracking needs are, and therefore vendors must create LIMS that are adaptable to individual environments. LIMS users may also have regulatory concerns to comply with such as [[CLIA]], [[Health Insurance Portability and Accountability Act|HIPAA]], [[Good Laboratory Practice|GLP]], and [[Food and Drug Administration|FDA]] specifications, affecting certain aspects of sample management in a LIMS solution.{{cite web |url=http://www.designworldonline.com/Regulatory-compliance-drives-LIMS/ |title=Regulatory compliance drives LIMS |author=Tomiello, Kathryn |publisher=Design World |date=21 February 2007 |accessdate=7 November 2012}} One key to compliance with many of these standards is audit logging of all changes to LIMS data, and in some cases a full [[electronic signature]] system is required for rigorous tracking of field-level changes to LIMS data.

====Instrument and application integration====

Modern LIMS offer an increasing amount of integration with laboratory instruments and applications. A LIMS may create control files that are “fed” into the instrument and direct its operation on some physical item such as a sample tube or sample plate. The LIMS may then import instrument results files to extract data for quality control assessment of the operation on the sample. Access to the instrument data can sometimes be regulated based on chain of custody assignments or other security features if need be.

Modern LIMS products now also allow for the import and management of raw assay data results.{{cite book |title=Ligand-Binding Assays: Development, Validation, and Implementation in the Drug Development Arena |author=Khan, Masood N.; Findlay, John W. |chapter=11.6 Integration: Tying It All Together |year=2009 |pages=324 |publisher=John Wiley & Sons |url=http://books.google.com/books?id=QzM0LUMfdAkC |isbn=0470041382 |accessdate=7 November 2012}} Modern targeted assays such as qPCR and deep [[sequencing]] can produce tens of thousands of data points per sample. Furthermore, in the case of drug and diagnostic development as many as 12 or more assays may be run for each sample. In order to track this data, a LIMS solution needs to be adaptable to many different assay formats at both the data layer and import creation layer, while maintaining a high level of overall performance. Some LIMS products address this by simply attaching assay data as [[Binary large object|BLOB]]s to samples, but this limits the utility of that data in data mining and downstream analysis.

====Electronic data exchange====

The exponentially growing volume of data created in laboratories, coupled with increased business demands and focus on profitability, have pushed LIMS vendors to increase attention to how their LIMS handles [[Electronic Data Interchange|electronic data exchanges]]. Attention must be paid to how an instrument’s input and output data is managed, how remote sample collection data is imported and exported, and how mobile technology integrates with the LIMS. The successful transfer of data files in Microsoft Excel and other formats, as well as the import and export of data to Oracle, SQL, and Microsoft Access databases is a pivotal aspect of the modern LIMS. In fact, the transition “from proprietary databases to standardized database management systems such as Oracle … and SQL” has arguably had one of the biggest impacts on how data is managed and exchanged in laboratories.{{cite web |url=http://www.starlims.com/Intl/AL-Wood-Reprint-9-07.pdf |format=PDF |title=Comprehensive Laboratory Informatics: A Multilayer Approach |author=Wood, Simon |publisher=American Laboratory |page=1 |date=September 2007 |accessdate=7 November 2012}}

====Additional functions====

Aside from the key functions of sample management, instrument and application integration, and electronic data exchange, there are numerous additional operations that can be managed in a LIMS. This includes but is not limited to:{{cite web |url=http://www.astm.org/Standards/E1578.htm |title=ASTM E1578 – 06 Standard Guide for Laboratory Information Management Systems (LIMS) |publisher=ASTM International |accessdate=7 November 2012}}

; [[audit]] management
: fully track and maintain an audit trail
; [[barcode]] handling
: assign one or more data points to a barcode format; read and extract information from a barcode
; chain of custody
: assign roles and groups that dictate access to specific data records and who is managing them
; compliance
: follow regulatory standards that affect the laboratory
; customer relationship management
: handle the demographic information and communications for associated clients
; document management
: process and convert data to certain formats; manage how documents are distributed and accessed
; instrument [[calibration]] and maintenance
: schedule important maintenance and calibration of lab instruments and keep detailed records of such activities
; inventory and equipment management
: measure and record inventories of vital supplies and laboratory equipment
; manual and electronic data entry
: provide fast and reliable interfaces for data to be entered by a human or electronic component
; method management
: provide one location for all laboratory process and procedure (P&P) and methodology to be housed and managed as well as connecting each sample handling step with current instructions for performing the operation
; personnel and workload management
: organize work schedules, workload assignments, employee demographic information, training, and financial information
; quality assurance and control
: gauge and control sample quality, data entry standards, and [[workflow]]
; reports
: create and schedule reports in a specific format; schedule and distribute reports to designated parties
; time tracking
: calculate and maintain processing and handling times on chemical reactions, workflows, and more
; traceability
: show audit trail and/or chain of custody of a sample
; workflows
: track a sample, a batch of samples, or a “lot” of batches through its lifecycle

===Client-side options===

A LIMS has utilized many architectures and distribution models over the years. As technology has changed, how a LIMS is installed, managed, and utilized has also changed with it.

”’The following represents architectures which have been utilized at one point or another:”’

====Thick-client====

A thick-client LIMS is a more traditional client/server architecture, with some of the system residing on the computer or workstation of the user ([[Client (computing)|the client]]) and the rest on the server. The LIMS software is installed on the client computer, which does all of the data processing. Later it passes information to the server, which has the primary purpose of data storage. Most changes, upgrades, and other modifications will happen on the client side.

This was one of the first architectures implemented into a LIMS, having the advantage of providing higher processing speeds (because processing is done on the client and not the server) and slightly more security (as access to the server data is limited only to those with client software). Additionally, thick-client systems have also provided more interactivity and customization, though often at a greater learning curve. The disadvantages of client-side LIMS include the need for more robust client computers and more time-consuming upgrades, as well as a lack of base functionality through a [[web browser]]. The thick-client LIMS can become web-enabled through an add-on component.{{cite web |url=http://www.scientificcomputing.com/Selecting-the-Right-LIMS.aspx |title=Selecting the Right LIMS: Critiquing technological strengths and limitations |author=O’Leary, Keith M. |publisher=Scientific Computing |accessdate=7 November 2012}}

====Thin-client====


A thin-client LIMS is a more modern architecture which offers full application functionality accessed through a device’s web browser. The actual LIMS software resides on a server (host) which feeds and processes information without saving it to the user’s hard disk. Any necessary changes, upgrades, and other modifications are handled by the entity hosting the server-side LIMS software, meaning all end-users see all changes made. To this end, a true thin-client LIMS will leave no “footprint” on the client’s computer, and only the integrity of the web browser need be maintained by the user. The advantages of this system include significantly lower cost of ownership and fewer network and client-side maintenance expenses. However, this architecture has the disadvantage of requiring real-time server access, a need for increased network throughput, and slightly less functionality. A sort of hybrid architecture that incorporates the features of thin-client browser usage with a thick client installation exists in the form of a web-based LIMS.

Some LIMS vendors are beginning to rent hosted, thin-client solutions as “[[software as a service]]” (SaaS). These solutions tend to be less configurable than on premise solutions and are therefore considered for less demanding implementations such as laboratories with few users and limited sample processing volumes.

Another implementation of the thin client architecture is the maintenance, [[warranty]], and support (MSW) agreement. Pricing levels are typically based on a percentage of the license fee, with a standard level of service for 10 concurrent users being approximately 10 hours of support and additional customer service, at a roughly $200 per hour rate. Though some may choose to opt out of an MSW after the first year, it’s often more economical to continue the plan in order to receive updates to the LIMS, giving it a longer life span in the laboratory.

====Web-enabled====

A web-enabled LIMS architecture is essentially a thick-client architecture with an added web browser component. In this setup, the client-side software has additional functionality that allows users to interface with the software through their device’s browser. This functionality is typically limited only to certain functions of the web client. The primary advantage of a web-enabled LIMS is the end-user can access data both on the client side and the server side of the configuration. As in a thick-client architecture, updates in the software must be propagated to every client machine. However, the added disadvantages of requiring always-on access to the host server and the need for cross-platform functionality mean that additional overhead costs may arise.

====Web-based====

Arguably one of the most confusing architectures, web-based LIMS architecture is a hybrid of the thick- and thin-client architectures. While much of the client-side work is done through a web browser, the LIMS also requires the additional support of [[Microsoft|Microsoft’s]] [[.NET Framework]] technology installed on the client device. The end result is a process that is apparent to the end-user through the Microsoft-compatible web browser, but perhaps not so apparent as it runs thick-client-like processing in the background. In this case, web-based architecture has the advantage of providing more functionality through a more friendly web interface. The disadvantages of this setup are more sunk costs in system administration and support for Internet Explorer and .NET technologies, and reduced functionality on mobile platforms.

=== Configurability ===

LIMS implementations are notorious for often being lengthy and costly.{{cite web|url=http://www.scientificcomputing.com/articles-In-Industry-Insights-Examining-the-Risks-Benefits-and-Trade-offs-of-Today-LIMS-033110.aspx |title=Industry Insights: Examining the Risks, Benefits and Trade-offs of Today’s LIMS |author=Royce, John R. |publisher=Scientific Computing |date=31 March 2010 |accessdate=7 November 2012}} This is due in part to the diversity of requirements within each lab, but also to the inflexible nature of LIMS products for adapting to these widely varying requirements. Newer LIMS solutions are beginning to emerge that take advantage of modern techniques in software design that are inherently more configurable and adaptable — particularly at the data layer — than prior solutions. This means not only that implementations are much faster, but also that the costs are lower and the risk of obsolescence is minimized.

==Distinction between a LIMS and a LIS==

Up until recently, the LIMS and [[laboratory information system]] (LIS) have exhibited a few key differences, making them noticeably separate entities.

*A LIMS traditionally has been designed to process and report data related to batches of samples from [[biology]] labs, [[Wastewater treatment plant|water treatment facilities]], [[Clinical trial|drug trials]], and other entities that handle complex batches of data. A LIS has been designed primarily for processing and reporting data related to individual patients in a clinical setting.{{cite web |url=http://labsoftnews.typepad.com/lab_soft_news/2008/11/liss-vs-limss-its-time-to-consider-merging-the-two-types-of-systems.html |title=LIS vs. LIMS: It’s Time to Blend the Two Types of Lab Information Systems |author=Friedman, Bruce |publisher=Lab Soft News |date=4 November 2008 |accessdate=7 November 2012}}{{cite web |url=http://www.analytica-world.com/en/news/35566/lims-lis-market-and-poct-supplement.html |title=LIMS/LIS Market and POCT Supplement |publisher=analytica-world.com |date=20 February 2004 |accessdate=7 November 2012}}
* A LIMS needs to satisfy [[good manufacturing practice]] (GMP) and meet the reporting and audit needs of the U.S. [[Food and Drug Administration]] and research scientists in many different industries. A LIS, however, must satisfy the reporting and auditing needs of hospital accreditation agencies, [[HIPAA]], and other clinical medical practitioners.
*A LIMS is most competitive in group-centric settings (dealing with “batches” and “samples”) that often deal with mostly anonymous research-specific laboratory data, whereas a LIS is usually most competitive in patient-centric settings (dealing with “subjects” and “specimens”) and clinical labs.

==Standards==
A LIMS covers standards such as:

* [[Title 21 CFR Part 11|21 CFR Part 11]] from the [[Food and Drug Administration (United States)]]
* [[ISO/IEC 17025]]
* [[ISO 15189]]
* [[Good laboratory practice]]

==See also==
* [[List of LIMS software packages]]
* [[Laboratory informatics]]
* [[Electronic lab notebook]]
* [[21 CFR 11]]
* [[Data management]]
* [[Scientific management]]
* [[Process Development Execution System]]

== Further reading ==

==References==

  1. a b “2011 Laboratory Information Management: So what is a LIMS?”. Sapio Sciences. 28 July 2010. Retrieved 7 November 2012.
  2. a b Vaughan, Alan (20 August 2006). “LIMS: The Laboratory ERP”. LIMSfinder.com. Retrieved 7 November 2012.
  3. ^ McLelland, Alan (1998). “What is a LIMS – a laboratory toy, or a critical IT component?” (PDF). Royal Society of Chemistry. p. 1. Retrieved 7 November 2012.
  4. a b c d e f “How Do I Find the Right LIMS — And How Much Will It Cost?” (PDF). Laboratory Informatics Institute, Inc. Retrieved 7 November 2012.
  5. a b c d Gibbon, G.A. (1996). “A brief history of LIMS” (PDF). Laboratory Automation and Information Management 32 (1): 1–5. doi:10.1016/1381-141X(95)00024-K. Retrieved 7 November 2012.
  6. ^ D. O. Skobelev, T. M. Zaytseva, A. D. Kozlov, V. L. Perepelitsa, and A. S. Makarova (2011). “Laboratory information management systems in the work of the analytic laboratory” (PDF). Measurement Techniques 53 (10): 1182–1189. doi:10.1007/s11018-011-9638-7. Retrieved 7 November 2012.
  7. ^ Tomiello, Kathryn (21 February 2007). “Regulatory compliance drives LIMS”. Design World. Retrieved 7 November 2012.
  8. ^ Khan, Masood N.; Findlay, John W. (2009). “11.6 Integration: Tying It All Together”Ligand-Binding Assays: Development, Validation, and Implementation in the Drug Development Arena. John Wiley & Sons. p. 324. ISBN 0470041382. Retrieved 7 November 2012.
  9. ^ Wood, Simon (September 2007). “Comprehensive Laboratory Informatics: A Multilayer Approach” (PDF). American Laboratory. p. 1. Retrieved 7 November 2012.
  10. ^ “ASTM E1578 – 06 Standard Guide for Laboratory Information Management Systems (LIMS)”. ASTM International. Retrieved 7 November 2012.
  11. ^ O’Leary, Keith M. “Selecting the Right LIMS: Critiquing technological strengths and limitations”. Scientific Computing. Retrieved 7 November 2012.
  12. ^ Royce, John R. (31 March 2010). “Industry Insights: Examining the Risks, Benefits and Trade-offs of Today’s LIMS”. Scientific Computing. Retrieved 7 November 2012.
  13. a b Friedman, Bruce (4 November 2008). “LIS vs. LIMS: It’s Time to Blend the Two Types of Lab Information Systems”. Lab Soft News. Retrieved 7 November 2012.
  14. ^ “LIMS/LIS Market and POCT Supplement”. analytica-world.com. 20 February 2004. Retrieved 7 November 2012.

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Overview of Study Designs in Clinical Research

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FDA | Adaptive Design Clinical Trials for Drugs and Biologics | Guidance for Industry


Guidance for Industry 

Adaptive Design Clinical Trials
for Drugs and Biologics
DRAFT GUIDANCE
This guidance document is being distributed for comment purposes only.
Comments and suggestions regarding this draft document should be submitted within 90 days of
publication in the Federal Register of the notice announcing the availability of the draft
guidance. Submit comments to the Division of Dockets Management (HFA-305), Food and
Drug Administration, 5630 Fishers Lane, rm. 1061, Rockville, MD 20852. All comments
should be identified with the docket number listed in the notice of availability that publishes in
the Federal Register.
For questions regarding this draft document contact Robert O’Neill or Sue-Jane Wang at 301­
796-1700, Marc Walton at 301-796-2600 (CDER), or the Office of Communication, Outreach
and Development (CBER) at 800-835-4709 or 301-827-1800.
U.S. Department of Health and Human Services
Food and Drug Administration
Center for Drug Evaluation and Research (CDER)
Center for Biologics Evaluation and Research (CBER)
February 2010
Clinical/Medical

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GUIDELINES FOR DESIGNING A CLINICAL STUDY PROTOCOL

GUIDELINES FOR DESIGNING A CLINICAL STUDY PROTOCOL
(based on International Conference on Harmonization, GCP Guidelines for Clinical Trial Protocol development)
To draft a sound scientific design of a clinical research study, the medical writer at the TGH,
Office of Clinical Research recommends that the following information should be included in
a research protocol. It will help facilitate the application submission process and study
approval from the Office of Clinical Research and IRB.
Study Summary:
Study summary should include the following:
• Protocol title (The title on related IRB submissions (e.g., applications for new study, changes in
procedure, research progress reports) must match the title of the protocol)
• Study phase
• Duration of the study
• Methodology
• Study site
• Approximate number of subjects
• Name, title, address, and telephone number of the PI, Co-PI, sponsors and study
coordinators
• Investigator’s affiliation
List of Abbreviations:
Provide a list of abbreviation used in the study protocol.
Background Information/Significance:
• Name and description of the investigational product, and in case of retrospective
reviews, justification for the chart and medical record reviews.
• Summary of results from prior clinical studies and clinical data to date.
• Human subjects risks and benefits.
• Description of the population to be studied.
• Description and justification for the dosing regimen and treatment period.
• A paragraph stating that the clinical study will be conducted in compliance with
the protocol, SOPs and the federal, state and local regulations.
• Citations from the references and data relevant to the study that also provides
background for the trial.
Objectives/Rationale/Research Question:
• Include a detailed description of the primary and secondary objectives and the
purpose of the study and clearly state your research hypothesis or your question.
• Discuss the project’s feasibility.
• Give details of resources, skills and experience to complete the study.
• Include any pilot study information.
Clinical Study Design:
• Primary and secondary endpoints, if any, to be measured during the study.
• Include the information that is needed to answer the research question. • Include the study design e.g. single, double-blind, observational, randomized,
retrospective etc. A schematic diagram of the study design would be helpful.
• Include the amount of dosage, dosing regimen of the drug, packaging and
labeling of the experimental drug.
• Explain how the study drug will be stored and dispensed.
• Include the expected duration of the study and subject’s participation and a
description of the sequence and duration of all study periods.
• Include any follow up visits.
• Describe when a subject’s participation in the trial may be discontinued.
• Maintenance of randomization codes and confidentiality.
• Describe the potential risks and steps taken to minimize the risks.
• Identify possible benefits of the study.
Inclusion and Exclusion criteria of the Subjects:
• Include subjects inclusion criteria.
• Include subjects exclusion criteria. Women of childbearing potential may not be
routinely excluded from participating in research, however, pregnant women
should be excluded unless there is a clear justification to include them.
• Include enrollment of persons of diverse racial and ethnic backgrounds to ensure
that the benefits of the research study are distributed in an equitable manner.
Informed consent form process:
• Provide information about the regulatory requirements of the consent form and which
languages will be used.
• Include a discussion of additional safeguards taken if potentially vulnerable subjects will be
enrolled in the study e.g., children, prisoners, cognitively impaired and critically ill
subjects.
• Specify Code of Ethics under which consent will be obtained.
• Include a copy of the proposed informed consent along with the protocol.
Adverse Event Reporting:
• Describe your plan to report any adverse event.
• Anticipated adverse events should be clearly documented.
• Identify the type and duration of follow up and treatment for subjects that
experience an adverse event.
Assessment of Safety and Efficacy:
• Be specific about the efficacy parameters.
• Include the methods and timing for assessing, recording, and analyzing efficacy
parameters.
• Specify the safety parameters.
• Record and report properly all the adverse events and inter current illnesses.
Treatment of Subjects:
• List all the treatments to be administered including product’s name, dose, route
of administration, and the treatment period for subjects.• Include all medication permitted before and during the clinical trial.
• Include the procedures for monitoring subject compliance.
Data Collection Plan:
• Define the type of data collection instrument that will be used and list all the
variables.
• Specify if computerized databases will be used.
• Identify what software will be used.
• Explain precautionary steps taken to secure the data.
Data Access:
• Inform who will have access to the data and how the data will be used. If data
with subject identifiers will be released, specify the person(s) or agency to whom
the information will be released and the purpose of the release.
• Address all study related monitoring, audits, and regulatory inspections.
Statistical Methods:
• Describe the statistical methods in detail.
• Include the number of subjects you are planning to enroll. For multi-center
studies, include the total number of sites expected and the total number of
subjects to be enrolled across all sites.
• Provide the rationale for the sample size, the calculations on the power of the
trial and the clinical justification.
• Procedure of accounting for missing, unused and spurious data.
• Procedures for reporting deviations from the original statistical plan.
• Include the selections of subjects to be included in the analyses.
Conflict of Interest:
Identify and document clearly any consultative relationship that the principal or coinvestigators has with a non-USF entity related to the protocol that might be considered a
real or apparent conflict of interest.
Publication and Presentation Plans:
List any meetings or conference you will be presenting the data and the results of your
study.
Timeline:
• A short paragraph stating when you plan to start and complete the study.
• Include a description e.g. subjects enrollment within a month, data collection
within 6 months etc.
References:
List all the references used in the back ground section at the end of the protocol.

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Designing a Clinical Study | Hypertension Guideline

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Understanding Clinical Trial Design: A Tutorial for Research Advocates

 

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