Using Computers in the Development of Pesticide Formulations

7 Using Computers in the Development of Pesticide Formulations 1

MARION F. BOTTS

Downloaded by CORNELL UNIV on October 7, 2016 | http://pubs.acs.org Publication Date: June 5, 1984 | doi: 10.1021/bk-1984-0254.ch007

Mobay Chemical Corporation, Kansas City, MO 64120 The cost of discovering and developing a new pesticide normally runs into the millions of dollars. With costs of this magnitude it is important that a l l tools available be evaluated for reducing development costs. One tool which is becoming more affordable and useful is the computer. With the appropriate software necessary for operating this tool, some possibilities are opening up for its use in the development of pesticide formulations. The study reported on here deals with the use of some commercially available computer programs and our use of them for developing pesticide formulations. Three commercially available software programs marketed by COMPUSERV INC. and used in this study are COED, RSM and PERSM. Computer technology i s r a p i d l y growing today. As a consequence of t h i s growing technology, computers are becoming more a f f o r d a b l e and u s e f u l (1). With the a p p r o p r i a t e software necessary f o r operating them, many uses are now being found f o r them. A computer i s a device which can r e c e i v e , s t o r e , and a c t on a set o f i n s t r u c t i o n s i n a given sequence. The i n s t r u c t i o n s can r e a d i l y be changed and the data upon which the i n s t r u c t i o n s a c t can be changed, too. The d i f f e r e n c e between i t and a programmable c a l c u l a t o r i s that the computer can handle text as w e l l as numbers This v e r s a t i l i t y gives the device an almost human-like data processing c a p a b i l i t y . There are some tasks the computer i s very good a t doing. There are other tasks which i t does not do w e l l . For example, i t can q u i c k l y do some of those tedious things that we f i n d hard to j u s t i f y i f they i n v o l v e a l o t of c a l c u l a t i o n or searching through data. I t can c a l c u l a t e and c a r r y out i n s t r u c t i o n s very q u i c k l y 1

Current address: PPG Industries, Inc., P.O. Box 31, Barberton, OH 44203

0097-6156/ 84/ 0254-0089506.00/ 0 © 1984 American Chemical Society

Scher; Advances in Pesticide Formulation Technology ACS Symposium Series; American Chemical Society: Washington, DC, 1984.

ADVANCES IN PESTICIDE FORMULATION TECHNOLOGY

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and a c c u r a t e l y and become a d e f i n i t e timesaver. On the other hand, i t i s not a very c r e a t i v e device because i t does only what i t i s t o l d to do. Someone has to give a computer exact and minute i n s t r u c t i o n s i n order to achieve a s p e c i f i c r e s u l t . They, indeed, are best s u i t e d for r e p e t i t i o u s and time-consuming t a s k s . Both conditions are u s u a l l y needed for t h e i r most p r o f i t a b l e use.


P e s t i c i d e Development Costs are High The cost of d i s c o v e r i n g and developing a new p e s t i c i d e runs i n t o m i l l i o n s of d o l l a r s (2-3). Part of t h i s cost can be a t t r i b u t e d to research necessary f o r producing s a t i s f a c t o r y f o r m u l a t i o n s . As formulations become more s o p h i s t i c a t e d the development costs become i n c r e a s i n g l y h i g h e r . In order to help reduce formulation development c o s t s , we need to c o n t i n u a l l y evaluate promising new tools for their cost-saving potentials. Statistical

Calculations

A l r e a d y , the microcomputer has proven u s e f u l to us i n making s t a t i s t i c a l c a l c u l a t i o n s f o r production c o n t r o l purposes. Figure 1 shows a g r a p h i c a l r e p r e s e n t a t i o n of some 297 T e c h n i c a l p u r i t y data p o i n t s . This example was produced by a microcomputer (Hewlett-Packard 9845B/System 45 Database) i n our Q u a l i t y Control Section. This s t a t i s t i c a l information i s h e l p f u l to the Formulation Chemist for designing formulations and a s s i g n i n g formula s p e c i f i c a t i o n s . It was produced q u i c k l y and a c c u r a t e l y with minimum e f f o r t . Literature

Searches

Another tedious and time-consuming task can be l i t e r a t u r e searches. We f i n d these searches can be handled q u i t e e a s i l y by computer. A person s k i l l e d i n searching the many databases a v a i l able can do so i n a very short time. Before t h i s study was s t a r t e d , s e v e r a l computer databases were searched to determine what information had been published concerning the t o p i c — "Using Computers i n the Development of P e s t i c i d e Formulations". Databases used for these searches i n cluded: Chemical A b s t r a c t s Conference Papers Index Engineering Conference/Meetings Engineering Index I n t e r n a t i o n a l Pharmaceutical A b s t r a c t s I n t e r n a t i o n a l Software Database Microcomputer Index N a t i o n a l T e c h n i c a l Information Service


91

Computer Development of Pesticide Formulations

7. BOTTS


30r

90.0

91.2

92.4

% active

Figure

1.

Pesticide

93.6

94.8

ingredient

Technical:1982.


96.0

92


The strategy v a r i e d according to the scope, coverage, p o i n t of view and s p e c i a l features of each database. B a s i c a l l y , the computer was asked to p u l l any c i t a t i o n s c o n t a i n i n g one or more synonymous terms from these concepts:


Pesticides Formulations Computerization No information was l o c a t e d which d e a l t s p e c i f i c a l l y with the design and e v a l u a t i o n of p e s t i c i d e f o r m u l a t i o n s . Some information was found on the use of computers f o r a n a l y z i n g p e s t i c i d e formulat i o n s and using mathematical models for s i m u l a t i n g r e l e a s e r a t e s of formulations ( 4 - 5 ) , but nothing on the a c t u a l development of a formulation per se. When the search strategy was changed to i n clude pharmaceutical f o r m u l a t i o n s , some p e r t i n e n t examples were found. Schwartz and co-workers ( 6 - 8 ) , f o r example, reported on computer o p t i m i z a t i o n techniques f o r pharmaceutical f o r m u l a t i o n s . Other workers reported on using computers i n storage s t a b i l i t y programs C9), developing cosmetic formulations (10), c o l o r matching of n a i l lacquers (11), and o p t i m i z i n g t a b l e t i n g processes and t a b l e t formulations (12-13). This l i t e r a t u r e search and our subsequent e v a l u a t i o n of some commercially a v a i l a b l e computer programs r e - e n f o r c e d the f a c t that computers are t o o l s and they need s k i l l e d people to use them. Computers have to be fed the r i g h t d a t a , and t h e i r output i s subj e c t to i n t e r p r e t a t i o n . This w i l l be f u r t h e r i l l u s t r a t e d when we consider the i n t e r p r e t a t i o n of a contour map produced by one of the programs to be mentioned. E v a l u a t i o n of Computer Programs The study reported on here deals with the e v a l u a t i o n of some comm e r c i a l l y a v a i l a b l e computer programs and our use of them f o r des i g n i n g and e v a l u a t i n g s e v e r a l p e s t i c i d e f o r m u l a t i o n s . To d e s c r i b e our use of these programs, a simple procedure w i l l be shown f o r the o p t i m i z a t i o n of an experimental s y n t h e t i c p y r e t h r o i d formulation FCR 1272 2EC. I t was o r i g i n a t e d by the parent company of Farbenfabriken Bayer GmbH, Leverkusen, West Germany. In world wide l a b o r a t o r y , greenhouse and f i e l d t e s t i n g i t has been proven to be an e x c e l l e n t non-systemic, f o l i a r i n s e c t i cide for the c o n t r o l of chewing i n s e c t s . Three computer programs used i n t h i s study were COED, RSM, and PERSM. These programs are p r e s e n t l y being marketed through CompuServe, I n c . , of Columbus, Ohio. COED, Computer Optimized Experimental Design, i s a p r o p r i e tary product of the B. F . Goodrich Company. This program was developed by the B. F . Goodrich Chemical D i v i s i o n s Statistical and Computer Group and has been used i n t e r n a l l y at B . F . Goodrich since January of 1975 (14). 1


7.

BOTTS


93

RSM, Response Surface Methodology, and; PERSM, P e r s p e c t i v e Views of RSM Surfaces, are programs marketed e x c l u s i v e l y through CompuServe, Inc. D e t a i l e d i n s t r u c t i o n s f o r operating these p r o grams w i l l not be discussed h e r e . These i n s t r u c t i o n s are r e a d i l y a v a i l a b l e from the vendor f o r those i n t e r e s t e d (14-16). A p o r t a b l e data t e r m i n a l with a c o u s t i c coupler f o r transmis s i o n of s i g n a l s by telephone was used to access both the computer and the programs. This route was a t t r a c t i v e because no c a p i t a l investment i n expensive programs or a computer was necessary.


Experimental Design In manufacturing p e s t i c i d e f o r m u l a t i o n s , s l i g h t overages i n a c t i v e i n g r e d i e n t are normally allowed for i n the formula to i n s u r e that l a b e l guarantees are s t i l l met a f t e r reasonable storage p e r i o d s . If too much overage i s allowed, a c t i v e i n g r e d i e n t i s given away unnecessarily and the cost to manufacture the product i s h i g h e r . If there are wide d i f f e r e n c e s i n p u r i t y f o r a p e s t i c i d e t e c h n i c a l , the formulation chemist needs t h i s information to b e t t e r design a p r a c t i c a l f o r m u l a t i o n . He must design the formulation i n such a way as to accommodate these p u r i t y v a r i a t i o n s . The COED program was f i r s t used i n t h i s study to design ex periments for d e s c r i b i n g the e f f e c t i n g r e d i e n t c o n c e n t r a t i o n has on emulsion performance. The COED program operates i n t h i s nanner. F i r s t , a set of independent v a r i a b l e s are s p e c i f i e d , rhese are each entered i n t o the program at given l e v e l s i n such a nanner as to cover a s p e c i f i c range f o r the v a r i a b l e . Next, pos s i b l e e f f e c t s from independent v a r i a b l e i n t e r a c t i o n s and c u r v i l i n e a r e f f e c t s on the v a r i a b l e s are d e s c r i b e d . L a s t , one or more dependent v a r i a b l e s to be acted upon are e n t e r e d The FCR 1272 2EC formula used here had already been p a r t i a l l y developed by t r a d i t i o n a l methods (Figure 2). I t contains four ingredients. f

Figure 2 Basic Formula f o r FCR 1272 2EC A c t i v e Ingredient Emulsifier A Emulsifier Β Solvent

(AI) @ 88%

29.7% 4.0% 3.0% 63.3% 100.0%

The concentration of three i n g r e d i e n t s i s s u f f i c i e n t to des c r i b e the formula given s i n c e the sum of a l l four must add up to 100%. These three concentrations were our independent v a r i a b l e s . Ranges f o r these independent v a r i a b l e s were set using the l e v e l s given i n Table I .


94



Table I .

Experimental Design V a r i a b l e s

Variable No,

Ingredient

1 2 3

AI @ 88% Emulsifier A Emulsifier Β

L e v e l s , % (w/w) 28.7 3.0 2.0

29.7 4.0 3.0

30.7 5.0 4.0

P r i o r c a l c u l a t i o n s showed that about 26% of a pure T e c h n i c a l would produce a 2 l b / g a l e m u l s i f i a b l e concentrate. This T e c h n i c a l normally averaged about 88% p u r i t y and would r e q u i r e 29.7% i n the formula on an "as i s " b a s i s . Levels 1% above and below t h i s value were chosen to give a reasonable range to f i t w i t h i n manufacturing specifications. E m u l s i f i e r s and e m u l s i f i e r l e v e l s were chosen as a r e s u l t of previous experience with s i m i l a r systems. A l l three v a r i a b l e s were s p e c i f i e d i n the program as being c u r v i l i n e a r and i n t e r a c t i o n s amongst them were considered. This allows the program to consider n o n l i n e a r equations i n i t s c u r v e f i t t i n g r o u t i n e . A f t e r the appropriate data had been entered i n t o the program, i t then generated 12 experiments to be performed. The chances of d e t e c t i n g d i f f e r e n c e s i n the dependent v a r i a b l e s sought were c a l c u l a t e d by the program as follows : Small Medium Large

59% 91% 99%

The experiments generated by the program were p r i n t e d out. They are l i s t e d i n Table I I . Table I I . Experiment No. 1 2 3 4 5 6 7 8 9 10 11 12

Computer Generated Experiments

1

V a r i a b l e Number 2

3

28.7 29.7 28.7 30.7 28.7 29.7 30.7 30.7 29.7 30.7 28.7 30.7

5.0 3.0 3.0 5.0 3.0 4.0 4.0 3.0 5.0 5.0 5.0 3.0

3.0 3.0 4.0 2.0 2.0 4,0 3.0 2.0 2.0 4.0 4.0 4.0


95


7. BOTTS

The dependent v a r i a b l e chosen to be measured was "Performance". The "Performance" v a r i a b l e was determined a c c o r d ing to the numerical scheme given i n Table I I I .


Table I I I .

Rating Scales Factor

Value Range

Foam Spontaneity Emulsification Emulsion S t a b i l i t y

0.5- -2.5 0.5- -2.5 1.0- -5.0 1.0- -5.0

The r a t i n g scheme was biased towards E m u l s i f i c a t i o n and Emulsion Stability. The use p a t t e r n f o r the product under study was the guide for t h i s b i a s . These two p r o p e r t i e s were considered more important i n the performance of the product than foam or spontaneity. The computer generated formulas were prepared and evaluated as a group. Numerical values were assigned for each property and t o t a l e d to give a "Performance" v a l u e . The r a t i n g scheme i s set up so that the higher number r e f l e c t s the b e t t e r performance. The "Performance" r a t i n g s experimentally obtained from the 12 experiments designed by the COED program are given i n Table IV, Table IV. Data Points for C a l c u l a t i n g "Performance" Experiment No.

Spontaneity

Emulsification

Emulsion Stability

1 2 3 4 5 6 7 8 9 10 11 12

2.0 1.0 1.0 2.5 2.5 1.5 2.0 2.0 2.5 2.0 2.0 1.0

5.0 2.0 1.5 5.0 5.0 5.0 5.0 4.0 5.0 5.0 4.0 2.5

5.0 2.0 1.0 3.0 3.0 4.0 5.0 3.0 3.0 5.0 5.0 1.0

Foam 1.3 1.5 1.5 1.5 1.5 1.5 1.3 1.5 1.5 1.3 1.3 1.5

Total Rating"Performance" 13.3 6.5 5.0 12.0 12.0 12.0 13.3 10.5 12.0 13.3 12.3 6.0

At t h i s stage, we get a rough idea of what i s happening. We can see that formulas 2, 3, and 12 give poor "Performance". To get more information from these data p o i n t s , they were next entered i n t o the Data A n a l y s i s p o r t i o n of the RSM program.



96


Using RSM f o r Data A n a l y s i s The Data A n a l y s i s p o r t i o n of RSM performs a s t a t i s t i c a l a n a l y s i s to v a l i d a t e the experimental r e s u l t s obtained i n the COED designed program. A set of contour maps i s then produced to i l l u s t r a t e how the dependent v a r i a b l e "Performance" v a r i e s as a f u n c t i o n of E m u l s i f i e r A and E m u l s i f i e r Β c o n c e n t r a t i o n . The v a r i a b l e AI i s h e l d constant. The program c a l c u l a t e d that 99.6% v a r i a b i l i t y could be e x p l a i n e d . Figures 3, 4, and 5 show the contour maps generated. The most important f a c t o r a f f e c t i n g the dependent v a r i a b l e i s p r i n t e d out on the x - a x i s . This i s i d e n t i f i e d as E m u l s i f i e r B. The second most important f a c t o r i s i d e n t i f i e d on the y - a x i s and i s E m u l s i f i e r A. The f a c t o r c o n t r i b u t i n g l e a s t to the "Performance" i s h e l d constant and i s i d e n t i f i e d as the a c t i v e i n g r e d i e n t ( A I ) . Numeric and alphanumeric characters are used to represent the; value of the dependent v a r i a b l e "Performance". Note i n F i g u r e 3 that "Performance" values of 15, 16 and 17 are p l o t t e d out on the contour map. These are p r o j e c t e d values which go beyond the l i m i t of the r a t i n g s c a l e that was set up. These kinds of occurrences again p o i n t to the f a c t that computers are t o o l s and t h e i r output should be subject to i n t e r p r e t a t i o n . As one becomes more adept at using these programs, these aberrations can sometimes be e l i m inated by using the proper r e s t r i c t i o n s allowed f o r i n the p r o gram. These contour maps (also c a l l e d response surfaces) give some u s e f u l i n s i g h t s i n t o the nature of the FCR 1272 2EC formulation being s t u d i e d . They i n d i c a t e that w i t h i n the l i m i t s s t u d i e d : 1. 2. 3.

The p u r i t y of the a c t i v e i n g r e d i e n t has l i t t l e e f f e c t on p h y s i c a l performance. E m u l s i f i e r Β has the most e f f e c t on "Performance", but i t s c o n c e n t r a t i o n can f l u c t u a t e s l i g h t l y without harm. E m u l s i f i e r A can f l u c t u a t e w i d e l y .

1

These are u s e f u l i n s i g h t s , but they should be kept i n p e r spective. This p a r t i c u l a r study d i d not take i n t o account other important v a r i a b l e s such as temperature and water hardness. The e f f e c t s produced by these other independent v a r i a b l e s can be handled separately i n other s t u d i e s , and t h e i r r e s p e c t i v e contour maps generated. These could a l s o be used as f a c t o r s i n c a l c u l a t ing the "Performance" v a r i a b l e . Using PERSM The equations generated by the Data A n a l y s i s p o r t i o n of the RSM program are compatible with the PERSM program f o r drawing 3-dimensional perspectives. To use these equations, the RSM f i l e c o n t a i n i n g them i s t r a n s f e r r e d to the PERSM program. PERSM w i l l l i s t the independent



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1 2 3 4 5 6 7 8 9

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Response Surface f o r Performance, FCR 1272 2EC, AI = 28.7%.

3.5

39 77 5 66 : 4 R 9 8 7 5 6 4 flfl 9 8 77 44 55 6 fl 99 88 4 66 5 B 9 AA 8 6 4 5 flfl 99 BB 77 44 55 66 A 9 7 8 6 . 4 5 flfl 77 99 88 6 .44 55 B A 7 8 9 66 .4 5 AA 88 6 .4 55 77 88 93 BB 6 A 8 7 9 5 66 B BB Afl 55 6 77 88 93 B AA 66 7 8 33 * 5 B B A 88 9 55 6 77 BB BB 8 99 AA . 5 66 7 B CCCCC 88 9 AA .55 6 77 B CC CCCC 8 99 A .5 66 7 B CC CCC 8 99 AA .5 66 77 BB CC CCC 88 9 AA 6 77 ! B CC CCC 8 99 Afl . 66 7 B CC CC B 88 99 fl . 6 77 B CC CCC 8 9 flfl . 66 7 CC cc 8 99 flfl B . 6 77 c cc 99 flfl BB .66 77 I cc ccc BB 9 fl *6 7 8i cc cc 99 flfl 77 8 c cc 99 flfl Ε . 77 88 cc ccc 99 flfl BE cc cc . 77 8 9 flfl BB c DDD CC .77 88 99 fl BB CC DDDDDD DDDDD CC .7 88 99 AA BB CC DDDD DDDD CC .7 8 99 AA BB C DDD DDD ccc B8 9 AA BB CC DDD DD cc . 88 99 AA BB C DD DDD . 88 99 AA BB CC CC DDD DD . 8 99 A B CC C DD DDD *88 99 Afl BB CC DDD CC D .8 9 Afl BB CC DD DD C cc 99 Afl DDD DD CC ccc . 99 AA DD C DD EEEEEEEE EEEEEEE cc . 99 Afl DD CC DDD EEEE EEE .99 flfl ce DD CC EEEE EEE OD .9 flfl CC D CC EEE EEE DDD .9 flfl I B CC DD CC EEE EEE DD . flfl Bl DD CC. CC EEE EE DD . fl BB DD C. CC EEE EE DD . AA BB D C. EEE EE DDD ccccc .flfl DD * EEE FFFFFFFFFFFFFFF EE DD cc «fl *

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Figure 5.

Response Surface for Performance, FCR 1272 2EC, AI - 30.7%.



100


v a r i a b l e s from the f i l e (2 to 10), two of which can be chosen f o r the χ and y axes. That i s followed by the dependent v a r i a b l e s , (1 to 8 ) , one of which can be s e l e c t e d as the response to be d i s played i n the z - d i r e c t i o n . Figures 6 and 7 show r e s u l t s obtained from our study with the FCR 1272 2EC i n s e c t i c i d e . Figure 6 shows AI h e l d at the low end of the range s t u d i e d and Figure 7 shows i t at the high end. It i s p o s s i b l e to h o l d AI constant at one i n t e r mediate value i n the range studied and to r o t a t e the 3-dimensional p e r s p e c t i v e through d i f f e r e n t angles f o r b e t t e r v i e w i n g . I t can be seen from these two f i g u r e s that the response s u r face i s saddle-shaped and has s l i g h t l y more t i l t at the low AI concentration. O v e r a l l t h e y ' r e very s i m i l a r and show that a c t i v e i n g r e d i e n t of the p u r i t y range s t u d i e d should perform s a t i s f a c t o r i l y i n this formulation. These are v i s u a l p e r s p e c t i v e s and i t i s very d i f f i c u l t to l o c a t e a c t u a l values from the drawing. Other experiments t r i e d with these three programs are i l l u s t r a t e d i n the f o l l o w i n g f i g u r e s : Figure 8 shows another experimental formula with a d i f f e r e n t solvent system. The a c t i v e i n g r e d i e n t c o n c e n t r a t i o n i s at a lower (200 g r a m / l i t e r ) l e v e l , a l s o . The e m u l s i f i e r l e v e l s t r i e d here are higher and the r e s u l t i n g response surface has a broader shape. Figure 9 shows a response surface f o r a commercial Dylox 1.5 O i l flowable f o r m u l a t i o n . The dependent v a r i a b l e of i n t e r e s t here is viscosity. It i s t y p i c a l l y l i n e a r with respect to thickener concentration. Figure 10 shows a response for a commercial M a t a c i l 180 O i l flowable f o r m u l a t i o n . Unexpectedly, we found the response surface to be n o n - l i n e a r with respect to thickener c o n c e n t r a t i o n . Conclusions In our e v a l u a t i o n of computers f o r developing p e s t i c i d e formula t i o n s , we f i n d them to have a d e f i n i t e p l a c e . They can carry out many tedious r o u t i n e s and free people f o r more c r e a t i v e t a s k s . They can q u i c k l y and a c c u r a t e l y r e t r i e v e , s t o r e and do s t a t i s t i c a l c a l c u l a t i o n s on s e t s of d a t a . They can g r a p h i c a l l y d i s p l a y the r e s u l t s of s t a t i s t i c a l c a l c u l a t i o n s f o r v i s u a l i n t e r p r e t a t i o n . They can do l i t e r a t u r e searches and they can help i n the o p t i m i z a t i o n of formulas. T h e i r p o t e n t i a l as powerful t o o l s f o r develop ing p e s t i c i d e formulations i s g r e a t . We must remember that they are s t i l l j u s t t o o l s , though. Computers and computer programs need s k i l l e d people to operate them. T h e i r value i s dependent upon t h e i r s k i l l f u l and imagina t i v e use. I t i s hoped that t h i s b r i e f d e s c r i p t i o n concerning our use of computers f o r developing p e s t i c i d e f o r m u l a t i o n s , at the A g r i c u l t u r a l D i v i s i o n of Mobay Chemical Corporation i n Kansas C i t y , M i s s o u r i , w i l l help s t i m u l a t e d i s c u s s i o n on t h i s s u b j e c t .



BOTTS


Figure 6. 28.7%.

3-Dimensional P e r s p e c t i v e , FCR 1272 2EC, AI

en

Figure 7. 30.7%.

3-Dimensional P e r s p e c t i v e , FCR 1272 2EC,




Figure 8. 19.3%.

3-Dimensional P e r s p e c t i v e ,

FCR 1272 200 E C , AI =

Figure 9. able.

3-Dimensional P e r s p e c t i v e ,

Dylox 1.5

O i l Flow-



BOTTS


Figure 10. Flowable.

3-Dimensional P e r s p e c t i v e , M a t a c i l 180 O i l


104


Acknowledgments The author would l i k e to acknowledge with deep a p p r e c i a t i o n the support given by h i s colleagues at the A g r i c u l t u r a l D i v i s i o n of Mobay Chemical C o r p o r a t i o n . He g r a t e f u l l y acknowledges the sug g e s t i o n f o r t h i s study by C . A . Anderson and the h e l p f u l comments afforded by J . Synek and W. H . Grimes i n the p r e p a r a t i o n of the manuscript. A l s o , C . T . Webb q u i c k l y and a c c u r a t e l y c a r r i e d out the computer l i t e r a t u r e search requests and updated them monthly. C. E . Evans k i n d l y provided microcomputer examples of s t a t i s t i c a l analyses from the Mobay Q u a l i t y C o n t r o l S e c t i o n .


Bibliography 1. The Kansas City Star, "Microcomputers: A New Way of Life" by Diane Stafford, February 27, 1983, p. 1G. 2. Anonymous, Chemical Week, July 26, 18 (1972). 3. Gilbert, Charles H. Farm Chemicals, "The Increasing Riskiness of the Pesticide Business", May No. 5, 21 (1977). 4. Coppedge, J. R.; Stokes, R. Α.; Ridgway, R. L . : Kinzer, R. E., U.S. Agric. Res. Serv., South Reg. 1976, p. 6. 5. Bozzay, J . ; Rusznak, I.; Torok, L.; Grega, J., Proc. Conf. Appl. Chem. Unit Oper. Processes, 3rd 1977, pp. 533-6. 6. Schwartz, J. B.; Flamholz, J. R.; Press, R. H., J. Pharm. Sci. 62:1165-1170 (July) 1973. 7. Ibid., J. Pharm. Sci. 62:1518-1519 (September) 1973. 8. Schwartz, J. B., J. Soc. Cosmet. Chem. 32:287-301 (SeptemberOctober) 1981. 9. Stone, Η. Α.; Slater, J. G., Pharm. Tech. 4:53-57 (April) 1980. 10. Roye, G. S.; Bernbe, G. R.; Busch, F. W., J. Soc. Cosmet. Chem. 24:783-795 (13) 1973. 11. Roye, G. S., Cosmet. Perfum. 90:25-32 (April) 1975. 12. Franz, R. M.; Banker, G. S.; Buck, J. R., J. Pharm. Sci. 69:621-628 (June) 1980. 13. Down, G. R. B.; Miller, R. Α.; Chopra, S. K.; Millar, J. F., Drug Dev. Ind. Pharm. 6:311-330 (4) 1980. 14. COED User's Guide CS-392, p.i. 15. RSM User's Guide CS-307. 16. PERSM User's Guide CS-413. RECEIVED February 9, 1984


Using Computers in the Development of Pesticide Formulations

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