PTT 202 Organic Chemistry for Biotechnology

Zulkarnain Mohamed Idris PTT 202 Organic Chemistry for Biotechnology zulkarnainidris@unimap.edu.my Lecture 1: General Principles of Analytical Organic Chemistry for Biotechnology Semester 1 2013/2014

Introduction • Analytical organic chemistry involves the use of laboratory methods to determine the composition of biological or organic samples. • The information gained from the analysis can be reported in two ways: 1. Qualitative report:indicates the presence of substance detected in a sample (Identification). 2. Quantitative report: states the amount or concentration of the substance present in a sample (Quantification).

Example: Qualitative report (Identification of carbohydrate sugars in unknown sample) HPLC (High Performance Liquid Chromatography) tr= 30 min tr= 30 min Identification of sugars present in the unknown sample was made by the comparison with the retention times (tr) of the standard sugars (sucrose, glucose, fructose, etc…) HPLC elution profile of ethanol extracts from petals of P. subulata for carbohydrates. (Left) standards, and (right) sample (stage 5): (1) sucrose, (2) glucose, (3) fructose, (4) myo-inositol, (5) sorbitol, A is 2-C-methyl-d-erythritol.

Example: Quantitative report (Quantification of carbohydrate sugars in unknown sample) Glucose concentration (µg/ml)

Selection of a valid method of analysis • To choose a suitable analytical method it is essential to know the chemical and physical properties of the test substance . • Not all methods that are suitable for qualitative analysis are also suitable for quantitative analysis. • Most analytical methods involve preparative steps before final measurement can be made. • Analytical methods validity (in terms of sensitivity, selectivity, accuracy/precision needed) • Need to consider the cost of the instrumentation and reagent required and also the time taken.

Instrumental methods • Most convenient methods are those that permit both identification and quantification but these types of methods are relatively few in number. • Atomic emission and absorption spectroscopy are good examples of instrumental methods that provide both qualitative and quantitative analysis. The wavelength of the radiation is used to identify the substance while the intensity of the radiation is used for its quantification. • If the substance is not easily detectable, some modification is done by chemical reaction to produce a substance that can be measured more easily.

Most of the classical methods (using complex reagents) have been superseded by the improved instrumental methods, but some very reliable still remain in use such as Folin Ciocalteu’s reagent for the detection of phenolic compounds. • Interference occurs when other substances as well as the test substance are detected by the method led to errors. If it is major problem, the sample must be partially purified before analysis. • Gas and liquid chromatography are analytical techniques that can be used to separate (purification) and quantify test substance in the sample sequentially.

Folin Ciocalteu assay-produce dark blue color when phenolic compounds are present. Colorless when no phenolic compounds are detected

Physiological methods • Involve a bioassay that measures the response of an organism or target organ to the test compound. • Can be conducted in vivo using animals or in vitrousing isolated organ or tissue preparations. • Many bioassays are quantitative but those that give only positive or negative result are to be quantal in nature (either qualitative or quantitative). • Bioassays must be designed to consider variations in measurements (since different animals/cells respond in different way to the same stimulus) and replicate measurement must be made using different animals/cells.

Due to the cost and ethical conflict of using animals in bioassays, the cell culture techniques (using cell lines) are introduced. Examples of bioassays using cell lines: VS Directly injected to animals Cell lines grown in petri dish and then the physiological studies done by in vitro assays

Assay kits • Developed methods marketed by reagent and instrument manufacturers. • Examples: colorimetric assays - which require the additional of chemical solutions to the test sample. • The kits include all the necessary standards and assay components. • Designed to be used in manual procedures or on particular automated instruments. • Full assay protocols, details of the composition of all reagents, hazard data, and specified storage conditions are given in the kits. • Reduced the necessity for individual laboratories to develop their own methods.

Assay kits (Examples) Glucose assay kits: a plate-based colorimetric enzymatic appraisal for the assurance of glucose in serum samples. Malaria rapid test kit: is a rapid chromatographic immunoassay for the qualitative detection of Human Malaria antigen in whole blood.

The quality of data • All data (particularly numerical) are subject to errors. • These types of errors should be quantified. Variability in analytical data are due to random and systematicerrors. Random error: • Represents the experimental uncertainty that occurs in any measurement (due to instrument design and use, e.g. frictional effects on balance, reading fluctuating signals). • Causes variation between replicate measurements and cannot be predicted and estimated. • Cannot be avoided but can’t be reduced by carefully technique and the use of good quality instruments.

Random error: Follows a normal distribution or Gaussian distribution about the mean. Frequency Histogram Normal distribution curve Class interval

Random error: • This type of error can be calculated statistically as standard deviation (s) of the data: where x is an individual measurement and n is the number of replicates. • For any number of replicates less than 30, s can be calculated as following:

Random error: • If limited number of replicates were done instead of single measurement, a greater degree of confidence could be placed in the resulting mean value and can be expressed as following (SEM = Standard Error of the Mean): • Replicates analyses have an advantage against single analysis by improving the degree of confidence, but the increased time and effort involved should be considered.

Random error: • Standard deviation permits a precise statement to be made regarding the distribution of the replicates measurements about the mean value. Relationship between standard deviation and the proportion of measurements about the mean value

Systematic error: • Constant in character and can be either avoided or corrected. • Cannot be measured or calculated by statistically. • Cause the shifting of the position of the meanof set of measurements relative to the original mean. • Such error shows bias towards either positive (an increase in the mean) or negative (a decrease in the mean). • This type of error due to instrumental factors (faulty equipment or uncalibrated instruments) and errors of method (failure to consider the limitations and constrains of a method or operate at different experimental conditions) that may result lower or greater reading than they should be.

Assessment of analytical methods • Analytical methods should be precise, accurate, sensitive and specific but due to errors, all methods fail to meet this criteria fully. Precision: • Reproducibility of results: a number of replicate measurements of a sample agree with one another. • Precision can be expressed in term of standard deviation. • Coefficient of variation (V) or relative standard deviation can be calculated as following : where s is standard deviation and is the mean value (see Procedure 1.1 in the Handout for example) .

Precision: • Variance ratio or ‘F’ test is used to compare the relative precision between two methods and can be calculated as following: • If the calculated value of F exceeds the critical value for F, (value from table) then it can be concluded that a significant difference does exist between the precision of the two methods (see Procedure 1.2 in the Handout for example).

Accuracy: • The closeness of the mean of set of replicate analyses to the true value of the sample. • The accuracy of the means of replicates of the one sample (e.g. same conc.) of two methods can be compared by ‘t’ test that can be calculated as following: • If the calculated value of t does not exceeds the critical value for t (value from table) then it can be concluded that no significant difference does exist between the accuracy of the two methods (see Procedure 1.3 in the Handout for example).

Accuracy: • The accuracy of series of different samples (e.g. different conc.) of two methods can be compared by the paired ‘t’ test that can be calculated as following: • Where is the mean value for the difference between the pairs (d), If the calculated value of t does not exceeds the critical value for t (value from table) then it can be concluded that no significant difference does exist between the accuracy of the two methods (see Procedure 1.4 in the Handout for example).

Accuracy: • The accuracy of series of different samples (e.g. different conc.) of two methods can also be compared by the correlation coefficient (r) that can be calculated as following: • If the calculated value of r is greater than 0.9 indicates fair to good correlation and together with an acceptable result for the paired ‘t’ test would provide strong evidence for a common degree of accuracy between the two methods (see Procedure 1.5 in the Handout for example).

Linear regression analysis: • The equation of straight line and the values for the slope and intercept can be calculated as following: • Where a is the slope and b is the intercept. • If these values differ from 1.0 and 0 respectively, the two methods differs from their accuracy (see Procedure 1.6 in the Handout for example).

Precision vs Accuracy bias bias

Sensitivity: • The ability of a method to detect small amount of the test substance. • The slope of the calibration graph is a conventional way of expressing sensitivity and particularly useful when comparing two methods. Specificity: • The ability to detect only the test substance. • Lack of specificity (due to interference effects) will result in false positive results if the methods is qualitative and positive bias in quantitative results (higher mean value than the true mean value).

Quality control in analytical methods Control charts: • A quality control chart (see Procedure 1.7 for example) is a time plot of a measured quantity that is assumed to be constant (with a Gaussian distribution) for the purpose of ascertaining that the measurement remains within a statistically acceptable range. • The control chart consists of a central line representing the known or assumed value of the control and either one or two pairs of limit lines, the inner control limit (warning limit) and outer control limit (action limit). • Warning limits:the max. and min. values within which a single control sample result is normally expected to lie (results are satisfactory and can be reported). • Action limits: specified max. and min. values outside which a single control sample result is extremely unlikely to lie without there being a serious error in the analysis (results are discarded, fresh standard are prepared, and the control sample re-analyzed).

Control charts: Action limits Warning limits

Accreditation of laboratories • Recognizes that a laboratory is competent to carry out its analytical services and a necessary requirement. • Several specific aspects of laboratory management which are essential in the process of accreditation as following: 1. Health and safety: • Provide a procedure hazard form (or material safety data sheets (MSDS)) that contain all the necessary information regarding potential hazards, mode of disposal and first aid procedures for all the chemicals. • Chemical hazards are classified into explosive, flammable, toxic, corrosive and irritant, and radioactive.

2. Standard operating procedures (SOPs): • A written details of the protocol that must be followed for any particular procedure being undertaken. • Include details of the procedures for collecting and handling the samples, performing the analysis, storing and retrieving data, and preparing report. 3. Computerization: • Computers plays a major role in modern laboratory. • LIMS: Laboratory Information Management System is a system that link various operations associated with both analytical and organization aspects.

4. Good laboratory practice (GLP): • Is a set of procedures within which the overall performance of a laboratory can be monitored. • Compliance with GLP may be required accreditation of a laboratory by external regulating agency. The features of GLP: • Staff- adequately trained with designated responsibilities and appropriate qualifications. • Equipment- adequate standard and full records of all maintenance and faults must be kept for 10 years. • Procedures-must in the form of SOP. • Data- details of the method, equipment, SOP and raw results must be stored for 10 years.

Sample of Analysis • The collection and storage of sample prior to analysis also affect the validity of a laboratory report. • The collection procedure must not adversely affect the analytical process. • The storage conditions should preserve the integrity of biological components. Examples of storage conditions of biological samples:

The production of results • The results of analysis should be presented in a clear manner to enable valid conclusion to be made. • Certain aspects of reporting analytical results that should be considered as following: • Choice of units: • SI units provide a system of universal units that consists primarily of 7 base units from which others may be derived. SI base units

Derived units

Prefixes

Calibration: • Involves analyzing solutions that contain a range of known concentrations (standard solutions) of the specified analyte in parallel with the test sample. • Determines the relationship between the reading and the concentration of the standard analyte (by plotting the calibration graph), and from this relationship, the amount of analyte in the test sample can be calculated. • Can be prepared by series of standard solutions (see Procedure 1.10 in Handout for example). • Graphical presentation of data: • Graphs produced for qualitative analytical purposes must be dated and include adequate information on the method analysis (such as SOP serial number, the analyte and the units of measurement). • The scale of each axis should be carefully chosen. • The linear relationship between two set of data can be expressed by linear equation derived from linear regression analysis.

Laboratory report: The report of any measurement must always include enough information to avoid misunderstanding and should contain specific details about the sample (e.g. see Table 1.10 as following):

PTT 202 Organic Chemistry for Biotechnology