Statistical and Continuous Manufacturing approach by Design of Experiment (DoE) for a Robust Synthetic Process of a Sorafenib Analogue

 

Shikha Saxena¹, Sandhya Bawa², Deepshikha Pande Katare³*

¹Amity Institute of Pharmacy, Amity University, Noida.

²Department of Pharmaceutical Chemistry, SPER, Jamia Hamdard, New Delhi.

³Centre for Medical Biotechnology, Amity Institute of Biotechnology, Amity University, Noida.

 

ABSTRACT:

Graphical-Statistical Design of experiment (DoE) is a new approach to reaction chemist over the age old technique of Quality by Testing. Modern era of ‘Continuous Manufacturing’, as a newly proposed ICH guideline Q13 introduces new concept of robust reaction designing bypassing the myriad of reactions involved in traditional approach as One Variable at a Time (OVAT). Present study demonstrates the amalgamation of complete reaction process with stat-software analysis of a novel multikinase inhibitor Sorafenib’s analogue using Design Expert ® Software. The Benzyl Analogue of Sorafenib is designed using Sorafenib as lead molecule. Each step of synthesis process involves the selection of solvents and bases scientifically and through Principal Component Analysis (PCA). Further, the key factors were established with low and high range as a preliminary study. The key factors like substrate molar concentration, temperature and time of reaction, were considered for DoE study wherein influence of factors and their interactions were studied over yield and quality of the product in a statistically relevant manner. For each step of reaction ‘Response Surface Methodology’ (RSM) was adopted with either Central Composite or Box-Behnken randomized matrix model design type. Synopsis for each DoE study was elaborated through analysis of variance (ANOVA) of defined model type. Data from non-linear studies have been shown through Contour plots, 3-D di-interaction plots and Coded Factor Interaction Reaction Equations. A cost effective and an unambiguous robust design space for each step of reaction was achieved with practical confirmation. The cost reduction of about 81.8% with exceptionally reduced number of experiments is obtained.

 

 

INTRODUCTION:

USFDA approved Sorafenib for the treatment of hepatocellular carcinoma and renal cancer.^1-2 Researchers have always shown a keen interest in designing analogues of existing drugs³.  For the complex synthesis of chemical moieties⁴, strategizing of all new reaction approach is always required. In such case, the involvement of proper channelization of DoE to achieve Quality by Design model proved to be a focus point. DoE gives a user friendly platform to reaction chemist. Various factors under reaction designing are temperature variations, compatibility of functional groups, time of reaction, reaction atmosphere, solvents selection etc. having direct effects on the reaction yield and purity⁵.

 

Using reaction designing robust conditions at which the reaction would give the best yield and purity of the compound. Among various professional DoE software available, Design-Expert® ver. 10.0.2.0 (Stat Ease Inc.) is one of the user friendly version and provides output in the form of ANOVA, contour plots and fact based designs. ICH guidelines Q8 to Q11 have discussed Quality by Design (QbD) implementation in active pharmaceutical ingredient (API) synthetic process and formulation development^6-8. ICH Q11 guideline discusses QbD approach for API synthesis whereas ICH Q13 guideline proposes for the concept of Continuous Manufacturing. The present study involving continuous manufacturing includes the control strategy with statistical design space for the chemical synthesis of N-benzyl analogue of Sorafenib (SRB-B); and comprises process optimization and Method Operable Design Region (MODR) overcoming the traditionally One Variable at a Time (OVAT) approach. Data input in the Design Expert® Software saves the chemist to carry out large number of experiments, and thus computing statistically derived viable design space.⁸ This design space gives a robust region of experimental complexities where synthesis of the product can be reached out considering the possible variables with results lying into 95% confidence interval. For reaction temperature and time, a specific low and high range of data has been chosen wisely at each step and Analysis of Variance (ANOVA), reflected the best fit.^9-10 Principal Component Analysis (PCA) is applied for the selection of solvent system.

 

In the present study, Sorafenib Analogue is synthesised via four step reaction process. The less and undeviating impact variables like solvents/bases selection and reaction atmosphere were included in preliminary study. Key variables having high experimental impact were selected and imported for model matrix design experiments.

 

MATERIALS AND REAGENTS:

Picolinic acid,  N-methyl pyrrolidone, pyridine, 4-dimethylaminopyridine, oxalyl chloride, tetramethylurea, triethylamine, N,N-dimethyl formamide were procured from Spectrochem (Spectrochem Pvt. Ltd., India); thionyl chloride, tetrahydrofuran, ethanol, methanol were procured from Rankem (Rankem Lab Chemical, India); benzylamine, 4-aminophenol, BOC anhydride, ethylene dichloride, dichloromethane and toluene were procured from CDH (Central drug house, India), 1-chloro-4-isocyanate-2(trifluoromethyl)benzene and 1-methylimidazole were procured from Sigma (Sigma Aldrich Corporation, USA). Nitrogen gas used to produce inert milieu was procured from Axcel (Axcel Gases Ltd., India).

 

RESULTS AND DISCUSSION:

Design space for Synthesis of 4-chloropicolinoyl chloride hydrochloride (Step 1)

 

Figure 1: (a) picolinic acid, (b) thionyl chloride and (c) 4-chloropicolinyl chloride (excess of hydrochloric acid entrapped)

 

Compound (a) was treated with excess of (b) in 1:2.2 ratio of mole equivalents in an inert atmosphere in the presence of N,N-dimethyl formamide (as per PCA) under Nitrogen gas (Fig 1). Enrico and group discussed about insilico designing using descriptor modelling¹¹. Based on software design 13 laboratory experiments were performed. Within a Response Surface methodology Central Composite model with a pre-defined coded low and high range of the factors (Table 1a) were finalised.

 

The analysis (response yield): ANOVA for the designated factors (table 1b) showed B & A² (p, 0.0002, <0.05), B² (p, 0.0017, <0.05) and A²B (p, 0.0013, <0.05) being the major interaction phase for high response. A (p, 0.1470, >0.05) and AB (p, 0.0837, >0.05) factors showed that alone temperature and temperature-time interaction phase was not responsible for the better response. The coded quadratic yield equation obtained was; Yield = +57.09 + 2.23A + 15.23B - 3.95AB - 12.01A² - 7.72B² - 15.24A²B. Overlay plot (fig 2a) demonstrates the response surface space; the interaction of increase in A with increase in B, rises the yield. 3D-design space (fig 2b) depicts factor coded red with points above predicted values. Based on α-0.05 two sided interval and reaction efficiency, the reaction was optimized with temperature range 70-75°C and time of reaction 20-24 hours. Reaction for (c) (excess of hydrochloric acid) found optimized with triplicate run for reacting (a) and (b) in an inert chamber, at 75°C till 24 hours using N,N-dimethyl formamide solvent. Treatment of (c) was done to remove excess of hydrochloric acid before proceeding further. A knowledge based structural data base needed to navigate kinase ligand interspace is approached by Linden and group⁸.

 

Table 1a: The imperative factor A (temperature of reaction) and factor B (time of reaction) with their low and high range considered for DoE of step1 reaction.

Factor	Name	Units	Type	Minimum	Maximum	Coded Low	Coded High	Mean	Std. Dev.
A	Temperature	°C	Numeric	25.00	125.00	-1 ↔ 40.00	+1 ↔ 110.00	73.46	28.78
B	Time	hours	Numeric	5.00	28.00	-1 ↔ 8.00	+1 ↔ 24.00	16.38	6.68

 

Table 1b: Response 1 (%yield); ANOVA for Reduced Quadratic model: Model F-value of 27.43 infers the model was quite significant. There was only 0.04% chance that a noise level could occur with this large F-value.

Source	Sum of Squares	df	Mean Square	F-value	p-value
Model	2398.87	6	399.81	27.43	0.0004	significant
A-Temperature	40.38	1	40.38	2.77	0.1470
B-Time	976.51	1	976.51	67.01	0.0002
AB	62.54	1	62.54	4.29	0.0837
A²	1009.98	1	1009.98	69.30	0.0002
B²	419.38	1	419.38	28.78	0.0017
A²B	472.56	1	472.56	32.43	0.0013

 

Figure 2: (a) Overlay plot demonstrates the response surface space; the interaction of increase in temperature and time of reaction. (b) 3D di-interaction design space depicts factor coded red with points above predicted values.

 

Design space for Synthesis of N-benzyl-4–chloropicolinamide (Step 2)

 

Figure 3: € 4-chloropicolinyl chloride, (d) benzylamine and € N-benzyl-4–chloropicolinamide

 

Intermediate product (c) from step 1 was treated with (d) (Fig 2). In the presence of toluene the purity was best accomplished and triethylamine was used to maintain the pH of reaction. DoE responses were obtained on the basis of reaction feasibility and scientific experience as yield and unreacted (d). Temperature, time of reaction and mole equivalent of (d) were considered to be key coded factors (Table 2a) for reaction isolated yield and amount of unreacted (d) level.^12-13 Based on software approach, practically 20 experiments were performed. The build information and design type was similar as specified in step 1.

 

The analysis (response yield): ANOVA for designated factors (Table 2b) indicated that the Model was extremely significant (p, <0.0001, <0.05). The imperial impact on the response was shown by C (p, <0.0001, <0.05). Other significant impact on response was shown by A (p, 0.0102, <0.05), whereas B (p, 0.0537, >0.05) alone has insignificant effect on the yield. However, the interaction of BC again showed the co-effect for better yield. The coded quadratic yield equation obtained was; Yield= +36.08 +4.31 A -2.98 B -30.84 C -2.22 AB -0.3518 AC -6.90 BC. 3D-design surface (Fig 4a) depicts factor coded red with points above predicted values as specific rise of yield at A 1.2 molar equivalent, B 1 hour and C 2°C as a centre robust point. Perturbation graph (Fig 4a) clearly shows that prominent effect on yield of the product is affected by deviation of C from the reference point.

 

The analysis (response Unreacted (d)): ANOVA for designated factors (Table 2c) showed that the model was extremely significant (p, <0.0001, <0.05). The obvious and definite influence on the response is shown by A (p, <0.0001, <0.05). However, unpredictably, unreacted (d) was also influenced by C (p, <0.0001, <0.05). So, unambiguously, C was an excellent factor for both the responses, and henceforth, a key factor for this reaction. A square effect i.e. A² and C² inferred that unreacted (d) responded in a square effect, which was ultimate sign among the reaction feasibilities. Thus, the straight inference was drawn that only A and C factors were responsible for forwarding the reaction. The coded quadratic yield equation obtained was; Unreacted (d)= -0.0037 + 0.2895A – 0.0045B +0.4521C -0.6862AC +0.1833 A² +0.7838 C² – 0.3887 A²C. 3D-design surface (Fig 4b) depicts factor coded blue for an end point of the reaction. So, Perturbation graph (Fig 4b) was taken into account, wherein typically high deviation effect of C on the response was seen. The factor B showed linear and zero effect on the response.

 

On the basis of response analysis, established yield of about 72% and purity 96.4% was achieved with reaction of 1 Meq of (c) and 1.3-1.4 Meq of (d) at 2-5°C temperature for about 1 hour in the presence of toluene as a solvent. Triethylamine was used to maintain the basic pH of the reaction slurry.

 

Table 2a: The imperative factor A (Meq of (d)), factor B (time of reaction) and factor C (temperature of reaction) with their low and high range considered for DoE of step2 reaction.

Factor	Name	Units	Type	Minimum	Maximum	Coded Low	Coded High	Mean	Std. Dev.
A	M eq (d)	Meq	Numeric	0.8636	1.54	-1 ↔ 1.00	+1 ↔ 1.40	1.16	0.1934
B	Time	min	Numeric	19.77	70.23	-1 ↔ 30.00	+1 ↔ 60.00	44.32	14.74
C	Temperature	°C	Numeric	2.00	50.00	-1 ↔ 2.00	+1 ↔ 50.00	16.18	14.22

 

Table 2b: Response 1 (%yield) of step2 reaction; ANOVA for Reduced Quadratic model: The Model F-value of 51.91 infers the model was quite significant. There was only 0.01% chance that a noise level could occur with this large F-value.

Source	Sum of Squares	df	Mean Square	F-value	p-value
Model	6513.72	6	1085.62	51.19	< 0.0001	significant
A-M eq (d)	183.10	1	183.10	8.63	0.0102
B-Time	92.94	1	92.94	4.38	0.0537
C-Temperature	5857.18	1	5857.18	276.16	< 0.0001
AB	45.62	1	45.62	2.15	0.1631
AC	0.4955	1	0.4955	0.0234	0.8806
BC	279.04	1	279.04	13.16	0.0025

Table 2c: Response 2 (Unreacted (d)) of step2 reaction; ANOVA for Reduced Quadratic model: The Model F-value of 52.78 infers the model was quite significant. There was only 0.01% chance that a noise level could occur with this large F-value.

Source	Sum of Squares	df	Mean Square	F-value	p-value
Model	4.60	7	0.6569	52.78	< 0.0001	significant
A-M eq (d)	0.9522	1	0.9522	76.50	< 0.0001
B-Time	0.0003	1	0.0003	0.0233	0.8809
C-Temperature	0.3318	1	0.3318	26.66	0.0001
AC	0.5909	1	0.5909	47.47	< 0.0001
A²	0.5194	1	0.5194	41.73	< 0.0001
C²	1.09	1	1.09	87.34	< 0.0001
A²C	0.1352	1	0.1352	10.87	0.0053

 

Figure 4: (a) 3D-di-interaction design space depicts factor coded red in response 1 and (b) 3D-di-interaction design space factor coded blue in response 2. (a) Perturbation graphs of response 1. (b) Perturbation graphs of response 2.

 

Design space for Synthesis of tert-butyl(4-(4-aminophenoxy) picolinoyl)(benzyl)carbamate (Step 3)

Figure 5: (e) N-benzyl-4–chloropicolinamide, (f) N-benzyl-4–chloropicolinamide BOC derivative (g) 4-aminophenol, (h) tert-butyl(4-(4-aminophenoxy) picolinoyl)(benzyl)carbamate.

 

Step 3 was two stepped reaction (Fig 5). Masking of secondary amine in (e), step 3(a) reaction was achieved with (e) and BOC anhydride using triethylamine and N, N-dimethyl formamide. Step 3b was finalized with reaction between (f) and (g) in the presence of N, N-dimethyl formamide. A concept of ‘microwave synthesis’ was applied; which provided dielectric heating wherein chemical reactions are enhanced¹⁴.

 

The key reaction factors for synthesis of (h) were temperature, time of reaction and mole equivalent of (g) with their low and high range value (Table 3a). The responses taken into consideration were yield and unreacted (g). Software designed 15 experiments which were performed with subjected randomized matrix model. Selection of DoE parameters through software were Study type: Response Surface and Box-Behnken model.^15-16

 

The analysis (response yield):

ANOVA for designated factors (Table 3b) inferred that the model was fairly significant (p, 0.0322, <0.05) with F-value of 12.02. For the direct factor effects, high impact on the response was showed by C (p, 0.0099, <0.05). Other significant impact by model terms on response was shown by B (p, 0.0166, <0.05). The interaction of AC (p, 0.0364, <0.05), BC (p, 0.0275, <0.05) again showed the combination of effect for better yield. The coded quadratic equation obtained was Yield = +76.811 + 1.750A + 5.254B - 8.751C - 2.338 - 3.223 AC - 7.001BC. The depiction of further interpretation was done with 3D-design space describing temperature and time effect over yield. 3D-design surface (Fig 6a) depicts factor coded red with points above predicted values with specific rise of yield at A (minor) about 1.2 molar equivalent, B (major) 12.5 minutes and C (major) 175°C as a centre robust point.

 

The analysis (response unreacted (g)):

ANOVA for designated factors (Table 3c) inferred with the Model F-value of 9.19 as fairly significant model. There is only a 0.22% chance that an F-value this large could occur due to noise. Results clearly showed that A (p, 0.0051, <0.05) and B (p, 0.0034, <0.05) were the major factors regulating the unreacted (g) left at the end of the reaction. The coded quadratic equation obtained was Unreacted (g) = +0.3060 +0.3303 A -0.3508 B + 0.0440 C -0.2792 AB. The unreacted (g) directly accounts for purity of the (h). By this rule, purity of (h) was directly proportional to the effect of A and B. The same inference was drawn with 3D-design space (Fig 6a) where near to 1.3 Meq of (g) and B between 17-20 minutes gave the least amount of unreacted (h) coded blue. Though, the broad reaction space gave the larger scope of reaction variability. As per the results, A 1.26-1.3 Meq of (g), B 17-20 minutes and C 160°C-190°C (~175°C), reaction confirmation was completed and tested in triplicate.

 

Table 3a: The imperative factor A (Meq of (d)), factor B (time of reaction) and factor C (temperature of reaction) with their low and high range considered for DoE of step3 reaction.

Factor	Name	Units	Type	Minimum	Maximum	Coded Low	Coded High	Mean	Std. Dev.
A	M eq (g)	Meq	Numeric	1.0000	1.30	-1 ↔ 1.00	+1 ↔ 1.30	1.14	0.1198
B	Time	min	Numeric	5.00	20.00	-1 ↔ 5.00	+1 ↔ 20.00	11.33	5.81
C	Temperature	°C	Numeric	100.00	250.00	-1 ↔ 100.00	+1 ↔ 250.00	176.67	53.84

 

Table 3b: Response 1 (%yield) of step3 reaction; ANOVA for Reduced Quadratic model: The Model F-value of 12.02 infers the model was fairly significant with cubical interactions. There was only 3.22% chance that a noise level could occur with this large F-value.

Source	Sum of Squares	df	Mean Square	F-value	p-value
Model	6772.11	11	615.65	12.02	0.0322	significant
A-M eq (g)	200.70	1	200.70	3.92	0.1421
B-Time	1212.77	1	1212.77	23.68	0.0166
C-Temperature	1757.03	1	1757.03	34.31	0.0099
AB	501.30	1	501.30	9.79	0.0521
AC	669.22	1	669.22	13.07	0.0364
BC	830.24	1	830.24	16.21	0.0275

 

Table 3c: Response 2 (Unreacted (g)) of step3 reaction; ANOVA for Reduced Quadratic model: The Model F-value of 9.19 infers the model was quite significant. There was only 0.22% chance that a noise level could occur with this large F-value.

Source	Sum of Squares	df	Mean Square	F-value	p-value
Model	2.62	4	0.6551	9.19	0.0022	significant
A-M eq (g)	0.9086	1	0.9086	12.74	0.0051
B-Time	1.03	1	1.03	14.50	0.0034
C-Temperature	0.0139	1	0.0139	0.1946	0.6685
AB	0.3301	1	0.3301	4.63	0.0569

 

 (a)

Figure 6: (a) 3D-di-interaction design space depicts factor coded red in response 1 and factor coded blue in response 2.

 

Design space for Synthesis of 4-(4-(3-(4-chloro-3-(trifluoromethyl)phenyl) ureido)phenoxy)-N-benzylpicolinamide (Step 4)

 

Figure 7: (h) tert-butyl(4-(4-aminophenoxy) picolinoyl) (benzyl)carbamate, (i) 1-chloro-4-isocyanate-2(trifluoromethyl)benzene and (j) 4-(4-(3-(4-chloro-3-(trifluoromethyl)phenyl)ureido)phenoxy)-N-benzyl picolinamide

 

Finally, the synthesis of (j) involved the fusion of (h) and (i) (Fig 7). Using dichloromethane as a solvent overtakes purity factor other solvents as shown in Table 4a. The reaction required an inert atmosphere which was provided with continuous flushing of N₂ gas. The key reaction factors (Table 4a) selected for the synthesis of (j) were Meq of (i), time and temperature of reaction. The analysis response taken into consideration were response yield, unreacted (i) and purity of (j). Software designed 14 experiments. These experiments were performed and results were put into the response section to obtain optimization space. Study type: Response Surface, Subtype: Randomized, Design type: Box-Behnken and design model: Quadratic.

 

The analysis (response yield):

ANOVA for designated factors (Table 4b) indicated that the model is fairly significant (p, 0.0001, <0.05). Most significant control on the response was shown by C (p, 0.0002, <0.05). Other significant impact on response is shown by B (p, 0.0021, <0.05) and A (p, 0.0134, <0.05). BC (p, 0.0008, <0.05) co-effect towards the response was thus the subject of study. The coded quadratic yield equation obtained was; Yield = +33.69 +5.25 A +7.27 B -11.12 C -12.04 BC -5.72 C². Overlay plot (Fig 8a) demonstrates the response surface space; the interaction of AB, BC and AC imposingly accounting for narrow, distant and specific robust reaction possibilities. With whole range of A i.e. 1 to 1.3 Meq, the impact over reaction yield is minimum. The yield grows with increase of A and B keeping C as constant at 35°C.

 

The analysis (response unreacted (i)): ANOVA for designated factors (table 4c) showed that the model is extremely significant (p, <0.0001, <0.05). A (p, <0.0001, <0.05) being the most significant impact on the response. Other significant impact on response is shown by B (p, 0.0001, <0.05). The coded quadratic yield equation obtained was Unreacted (i) = +0.2056 +1.17 A -0.5250 B +0.0250 C +1.11 A² +0.2644 B². Keeping C being constant at 35°C as inferred in the analysis of response 1, overlay plot figure  for response 2 (Fig 8a) depicted factor coded blue with points below predicted values. With decrease of A i.e. from 1.3 to 1.0 Meq, the response desirably diminished. B was restricted to 20 minutes. The same has been depicted in fig 8b in the contour plots in AB interaction.

 

The analysis (response purity of (j)): ANOVA for designated factors (table 4d) showed that the model is fairly significant (p, 0.0058, <0.05). C (p, 0.0016, <0.05) being the most significant impact on the response. Low F-values for A, B, and C² showed minor influence of these factors over purity. The coded quadratic yield equation obtained was Purity (j) = +96.84 - 0.1125A -0.5323 B - 3.71 C - 2.50 BC – 2.63 C². In overlay plot (fig 8a), linear and least effect of A and B was seen keeping C as constant. That clearly show that only C had an effect on the response. BC interaction gives a fairly good and desirable purity of about 99.4-99.6%. Increase in A, certainly decreased the response 3.

 

As per the analysis of the responses, the reaction for (j) found optimized with triplicate run for reacting (h)-1.0 Meq and (i) 1.12 to 1.17 Meq in an inert chamber, at 32-37°C stirring for 17-22 minutes using dichloromethane as a solvent.

 

Table 4a: The imperative factor A (Meq of (d)), factor B (time of reaction) and factor C (temperature of reaction) with their low and high range considered for DoE of step4 reaction.

Factor	Name	Units	Type	Minimum	Maximum	Coded Low	Coded High	Mean	Std. Dev.
A	M eq (h)	Meq	Numeric	1.0000	1.30	-1 ↔ 1.00	+1 ↔ 1.30	1.15	0.1177
B	Time	min	Numeric	5.00	20.00	-1 ↔ 5.00	+1 ↔ 20.00	13.57	6.02
C	Temperature	°C	Numeric	20.00	50.00	-1 ↔ 20.00	+1 ↔ 50.00	35.00	11.77

 

Table 4b: Response 1 (%yield) of step4 reaction; ANOVA for Reduced Quadratic model: The Model F-value of 25.20 infers the model is fairly significant. There is only a 0.01% chance noise could occur with this large F-value.

Source	Sum of Squares	df	Mean Square	F-value	p-value
Model	2781.37	5	556.27	25.20	0.0001	significant
A-M eq (i)	220.50	1	220.50	9.99	0.0134
B-Time	442.12	1	442.12	20.03	0.0021
C-Temperature	939.51	1	939.51	42.55	0.0002
BC	612.01	1	612.01	27.72	0.0008
C²	112.08	1	112.08	5.08	0.0543

 

Table 4c: Response 2 (Unreacted (i)) of step4 reaction; ANOVA for Reduced Quadratic model: The Model F-value of 75.22 implies the model is significant. There is only a 0.01% chance that an F-value this large could occur due to noise.

Source	Sum of Squares	df	Mean Square	F-value	p-value
Model	17.54	5	3.51	75.22	< 0.0001	significant
A-M eq (i)	11.04	1	11.04	236.89	< 0.0001
B-Time	2.20	1	2.20	47.29	0.0001
C-Temperature	0.0050	1	0.0050	0.1072	0.7517
A²	4.11	1	4.11	88.09	< 0.0001
B²	0.1759	1	0.1759	3.77	0.0880

 

Table 4d: Response 3 (Purity of (j)) of step4 reaction; ANOVA for Reduced Quadratic model: The Model F-value of 7.92 implies the model is significant. There is only a 0.58% chance that an F-value this large could occur due to noise.

Source	Sum of Squares	df	Mean Square	F-value	p-value
Model	189.27	5	37.85	7.92	0.0058	significant
A-M eq (i)	0.1013	1	0.1013	0.0212	0.8879
B-Time	2.37	1	2.37	0.4962	0.5012
C-Temperature	104.49	1	104.49	21.86	0.0016
BC	26.44	1	26.44	5.53	0.0465
C²	23.66	1	23.66	4.95	0.0567

 

 

Figure 8: (a) Overlay plot demonstrates the interaction between AB, BC and AC factors for response 1, 2 and 3, with robust response space.

 

CONCLUSION:

Statistical optimization of synthetic route gives a platform for the cost effective and robust method so as to get the better yield and purity. DoE in combination with stat-software based tool overcomes the OVAT approach pertaining long and repetitive reaction and unreliable results. Derivation of the coded stat equations for each of the four step reaction of SRB-B allows to study directly the main effect and co-effects factors i.e. reaction time, temperature and unreacted substrate, calculating the response in the form of yield and purity. The results obtained through RSM with Design Expert ® software represented the total extrapolated number of combinations of experiments upto 100experiments/response/reaction. This resulted into a process development with cost reduction of about 81.8% with exceptionally reduced number of experiments. Far reached scope of microwave synthesis decreased the reaction time upto about 82%. The combination of wide reaction variability assessment and closely monitored quantitative statistically analysed results ensured a Continuous Manufacturing without waiting for the quality testing results from the analytical laboratory.

 

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Received on 09.07.2019           Modified on 18.08.2019

Accepted on 26.09.2019         © RJPT All right reserved