Collaborative Evaluation of Commercially Available Automated

Article Cite This: Org. Process Res. Dev. 2018, 22, 1500−1508

pubs.acs.org/OPRD

Collaborative Evaluation of Commercially Available Automated Powder Dispensing Platforms for High-Throughput Experimentation in Pharmaceutical Applications Matthew N. Bahr,*,†,■ David B. Damon,‡,■ Simon D. Yates,§ Alexander S. Chin,∥ J. David Christopher,¶ Samuel Cromer,†,#,▲ Nicholas Perrotto,○ Jorge Quiroz,⊥ and Victor Rosso◆

Downloaded via IOWA STATE UNIV on January 4, 2019 at 02:33:02 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

†

GlaxoSmithKline, Pharmaceutical Research and Development, Platform Technology & Science, 1250 South Collegeville Road, Collegeville, Pennsylvania 19426, United States ‡ Pfizer Inc., Worldwide Research and Development, Pharmaceutical Sciences Small Molecule Chemical Research and Development, Eastern Point Road, Groton, Connecticut 06340, United States § AstraZeneca, Pharmaceutical Technology & Development, Chemical Development, Silk Road Business Park, Macclesfield, Cheshire SK10 2NA, United Kingdom ∥ Merck & Co., Inc., MRL, Preformulation, 126 East Lincoln Avenue, Rahway, New Jersey 07065, United States ¶ Merck & Co., Inc., MRL, Research CMC Statistics, 770 Sumneytown Pike, West Point, Pennsylvania 19486, United States # Drexel University, College of Engineering, 3141 Chestnut Street, Philadelphia, Pennsylvania 19104, United States ○ Merck & Co., Inc., MRL, Process R&D, 126 East Lincoln Avenue, Rahway, New Jersey 07065, United States ⊥ Merck & Co., Inc., MRL, Research CMC Statistics, 2000 Galloping Hill Road, Kenilworth, New Jersey 07033, United States ◆ Bristol-Myers Squibb, Global Product Development & Supply, Chemical & Synthetic Development, One Squibb Drive, New Brunswick, New Jersey 08903, United States S Supporting Information *

ABSTRACT: Many workflows in Pharmaceutical R&D involve the manipulation of defined amounts of powders. Automated powder dispensing platforms are currently available; however, these existing technologies do not meet the requirements for every high-throughput experimentation powder dispensing application. A Working Group (WG) composed of pharmaceutical researchers within the Enabling Technologies Consortium (ETC) evaluated automated platforms commercially available from three manufacturers using an objective, systematic testing protocol. This paper describes the selection of powders and testing conditions used in this evaluation, and it assesses the impact that the powders, testing conditions, equipment environment, and other factors had on the performance of the selected platforms. KEYWORDS: high throughput experimentation, powder dispensing, laboratory automation, pharmaceutical research

■

INTRODUCTION One of the primary goals of pharmaceutical R&D is to deliver the best possible medicine to the patient in a timely and costeffective manner. Regulatory and economic pressures have led to increasing demands for streamlining operations, reducing costs, and transformative innovations.1 To address these demands, a variety of laboratory automation systems have been developed, which in turn have facilitated the growth of high-throughput experimentation (HTE). HTE allows for the rapid, parallel evaluation of a large number of variables in a timely and material-efficient manner, and it has been applied to both active pharmaceutical ingredient (API) and formulation (Drug Product) research.2 HTE automation has enabled researchers to optimize the chemical processes used to prepare therapeutic candidates,3 to identify new solid forms of APIs,4 and to measure physicochemical properties such as solubility5 and stability.6 Accurate and precise powder dispensing is a critical and ubiquitous component of many HTE studies. The past decade has seen the development of automated powder dispensing © 2018 American Chemical Society

technologies and their integration into API and drug product research.7 These technologies have facilitated HTE and helped increase research efficiency and breadth. Additionally, these technologies feature automated data recording systems which enhance data integrity and provide increased worker safety through less exposure to chemicals and fewer repetitive manual tasks. Despite these advances, however, currently available automated powder dispensing platforms do not meet the needs for every HTE powder-dispensing application. Desired dispense amounts may be outside the range of equipment capabilities, instrument configurations may not be compatible with desired vial sizes or plate configurations, and the powders simply may not dispense with required accuracy and precision. The wide range of physical properties exhibited by commonly used powders undoubtedly adds to the complexity of HTE powder dispensing. Received: August 9, 2018 Published: October 9, 2018 1500

DOI: 10.1021/acs.oprd.8b00259 Org. Process Res. Dev. 2018, 22, 1500−1508

Organic Process Research & Development

Article

Table 1. Commercially Available Automated Powder Dispensing Platforms Chemspeed SWING w/SDU Dispenser

Mettler-Toledo Quantos QB5

Unchained Laboratories Freeslate (CM3)

Unchained Laboratories Junior (Protégé)

dispense heads

15 mL type 1, type 2 15 mL type 1D, type 2D 15 mL type 4

QH012-LNMP dosing head

classic hopper SV hopper

classic hopper SV hopper

run setting

teach mode

AlgoM/Algo P

N/A

N/A

purge box

fume cupboard glove box LEV purge box

glove box open bench

LEV

BMS MSD Pfizer

AstraZeneca GSK Pfizer

AstraZeneca BMS MSD Pfizer

GSK

vendor

equipment location

test sites

Table 2. Powders Evaluated in This Study powder

grade

vendor

lot number

D-mannitol

USP Aerosil 200 Pharma USP BCR Reference Material Kollidon CL Redi-Dri USP

Sigma-Aldrich Evonik Chem-Impex Sigma-Aldrich BASF Sigma-Aldrich Chem-Impex

SLBL0926 V 305070200 40610 0256 39970156P0 SLBL3513 V 000854−1201407384

fumed silica L-proline limestone powder polyvinylpolypyrrolidone (PVPP) sodium chloride thiamine HCl

The authors acknowledge that the powder dispensing platforms used in this study may not represent the most current systems available from the manufacturers selected. Run Data from Automated Powder Dispensing Platform Experiments. The mass dispensed (mg) and dispense time (sec) data were obtained from the automated powder dispensing systems. The dispense time refers to the time required for a given powder dispense to a well to complete, and it does not include the time required to move the vial, plate, or dispense head into position prior to the dispense. Aggregated mean, median, standard deviation, and % RSD calculations on mass dispensed and dispense time data were determined in Spotfirec and verified in Excel.d The % error of each mass dispensed was defined as the difference between the mass dispensed and the target mass, divided by the target mass, and multiplied by 100% (see eq 1).

Until now, a systematic evaluation of commercial platforms for HTE powder dispensing applications has not been reported. The results of such a study may improve pharmaceutical R&D productivity by providing researchers with more guidance on conditions to select for a particular application on a given platform, and it could inspire equipment manufacturers to develop new technologies and fill gaps in existing platform capabilities. The HTE Working Group (WG) of the Enabling Technologies Consortium (ETC)a,8 was formed in 2015 to improve the ability of researchers to conduct HTE powder dispensing. To accomplish this, an objective, systematic evaluation of current automated powder dispensing platforms was conducted. Herein we report the design, execution, and analysis of this evaluation and conclude with suggested future directions for HTE automated powder dispensing platforms.b

■

ij (mass dispensed) − (target mass) yz zz × 100% % error = jjj zz j target mass k {

MATERIALS AND METHODS Four commercially available, off-the-shelf, automated powder dispensing platforms from three manufacturers were selected for this study (Table 1). The platforms selected are routinely used in powder dispensing at the laboratories that participated in this evaluation, and are fit for purpose. All systems use gravity to deliver the powder from the dispense head through a funnel to the vial. Each platform used its own dispense heads to contain the powder and dispense it into vials. Depending on the particular platform and dispense head selected, rotary and/ or tapping actions were used to facilitate powder flow through the dispense head. The Chemspeed SWING SDU and Unchained Laboratories classic dispense head technologies used rotary action, the Unchained Laboratories SV head technology used tapping action, and the Mettler-Toledo Quantos technology used both rotary and tapping actions.

(1)

Powders. Seven powders were selected to represent a range of physical properties typically encountered in a pharmaceutical R&D laboratory, and they are listed in Table 2. The same grade, vendor, and lot of each powder were used for testing on all automated powder dispensing systems across the five companies, locations, and test environments. Study Scope, Design, and Workflow. The study included one-to-many and many-to-many automated powder dispensing platforms. The study design tested practical situations routinely encountered in HTE workflows in pharmaceutical R&D laboratories. The design was standardized as much as possible and included the following aspects: 1501



Article

Table 3. Number and % of Dispenses Retained for Analysis Broken down by Powder and Equipmenta powder D-mannitol

fumed silica limestone powder L-proline PVPP sodium chloride thiamine HCl total

Chemspeed SWING

Mettler-Toledo Quantos QB5

Unchained Laboratories Freeslate

Unchained Laboratories Junior

total

650 (51.6%) 0 (0%) 314 (87.2%) 294 (54.4%) 691 (76.8%) 720 (100%) 107 (29.7%) 2776 (67.1%)

680 (100%) 239 (99.6%) 600 (100%) 358 (99.4%) 720 (100%) 537 (99.4%) 558 (100%) 3692 (99.8%)

895 (99.4%) 0 (0%) 360 (100%) 689 (95.7%) 533 (98.7%) 440 (81.5%) 833 (92.6%) 3750 (94.7%)

1336 (98.2%) 240 (100%) 540 (100%) 360 (100%) 720 (100%) 2042 (99.2%) 719 (100%) 5957 (99.3%)

3561 (84.8%) 479 (99.8%) 1814 (97.5%) 1701 (85.9%) 2664 (92.5%) 3739 (96.9%) 2217 (87.4%) 16175 (90.9%)

a

Attempts to dispense fumed silica on the Chemspeed SWING and Unchained Labs Freeslate systems were unsuccessful.

Figure 1. % error vs dispense number on Chemspeed SWING systems. Scatterplot of % error values (−25% to +100%) vs dispense number (1− 20), broken down by powder (columns), target mass in mg (rows), and dispense head (colors).

their flow properties. A principal component analysis (PCA) was conducted on the physical property data to ensure that powders considered in the study spanned a wide range of physical properties. The powder characterization and PCA are included in the Supporting Information. A data verification process identified outliers and recurring patterns in the data from the 17 797 dispenses. From this, 1622 dispenses were discounted from the analysis, and 16 175 dispenses were retained for analysis. Discounted dispenses fell into one of four categories: Machine stall: a run which aborted prior to its desired conclusion due to lack of powder flow during a given dispense (103 dispenses = 0.6%) Machine time out: a dispense which ended due to a time out error caused by low powder flow, but the run continued with the subsequent dispense (153 dispenses = 0.9%) Questionable value: a dispense with an outlier amount of mass dispensed (39 dispenses = 0.2%) Zero value: an intended dispense in which no powder was dispensed to a single vial in a run (67 dispenses = 0.4%) or to

Vials: 8 × 30 mm 1 mL shell vials. Target mass: 2, 10, and 50 mg.e Fumed silica was only tested at 2 and 10 mg since 50 mg would not fit in a 1 mL vial. Number of runs: 3 runs for each powder at each target mass, for a total of 6 runs for fumed silica and 9 runs for all other powders.f Number of dispenses: 1 run = 20 dispenses of a powder at a given target mass into 20 separate vials.g For a given configuration of automated powder dispensing platform/ dispense head/setting, 120 dispenses were made for fumed silica and 180 dispenses for all other powders. A total of 17,797 dispenses were made in the study. Run order: The run order intentionally avoided conducting three 2 mg runs in sequence, followed by three 10 mg runs and then three 50 mg runs. The run order was varied to evaluate how systems would adapt to changing target masses for the same powder from run to run.h

■

RESULTS AND DISCUSSION Each powder was characterized by a variety of methods commonly used in pharmaceutical R&D to assess powders and 1502



Article

Figure 2. % error vs dispense number on Mettler-Toledo Quantos QB5 systems. Scatterplot of % error values (−25% to +100%) vs dispense number (1−20), broken down by powder (columns), target mass in mg (rows), and equipment location (colors).

Figure 3. % error vs dispense number on Unchained Laboratories Freeslate systems. Scatterplot of % error values (−25% to +100%) vs dispense number (1−20), broken down by powder (columns), target mass in mg (rows), and equipment location/dispense head (colors).

for any target mass level on the Chemspeed SWING or Unchained Laboratories Freeslate systems.j Chemspeed SWING. The data from the 2776 (67.1%) Chemspeed SWING powder mass dispenses were analyzed for % error as shown in Figure 1, which shows a scatterplot of % error values vs dispense number through a run, broken down by the powder being dispensed, the target mass, and the

the remaining vials in a run following a machine stall (1260 dispenses = 7.1%)i The distribution of dispenses retained for analysis broken down by powder and instrument platform is displayed in Table 3. Fumed silica has a very low bulk density, which made it not possible to dispense at the 50 mg target mass level, as well as 1503



Article

Figure 4. % error vs dispense number on Unchained Laboratories Junior system. Scatterplot of % error values (−25% to +100%) vs dispense number (1−20), broken down by powder (columns), target mass in mg (rows), and dispense head (colors).

dispense head type.k As the target mass values increased from 2 to 10 to 50 mg, the % error values improved for all powders. The Chemspeed platform has an optional operation called “Teaching” which performs several initial dispenses to set dispensing parameters for all subsequent dispenses. Teaching for each powder occurred prior to each set of 180 dispenses, thus the variability in % error was relatively constant.l All dispenses on the three Chemspeed SWING systems were performed in an equivalent purge box environment. Mettler-Toledo Quantos QB5. The data from 3692 (99.8%) Mettler-Toledo Quantos QB5 powder mass dispenses were analyzed for % error as shown in Figure 2.m As the target mass values increased from 2 to 10 to 50 mg, the % error improved for all powders. The Mettler-Toledo dosing head contains an embedded RFID chip which utilizes a learning algorithm during the initial dispenses of a run to optimize accuracy and speed.n For runs with D-mannitol, L-proline, PVPP and thiamine HCl, the algorithm learned dosing parameters quickly. For a few runs with fumed silica, limestone powder, and sodium chloride, several dispenses were required for the algorithm to optimize. The % error values were slightly lower for the system located in the glovebox environment compared with the other environments.o Overall, the laboratory environment had little impact on the % error values. The combination of rotary and tapping actions in the Mettler-Toledo dispense head technology could contribute to its general utility observed in this study. Unchained Laboratories Freeslate (Formerly Freeslate CM3). The data from 3750 (94.7%) powder dispenses on the Unchained Laboratories Freeslate system were retained for analysis. The mass dispensed data from the retained Unchained Laboratories Freeslate dispenses were analyzed for % error as shown in Figure 3.p As the target mass values increased from 2 to 10 to 50 mg, the % error values improved

for all powders. Unchained Laboratories powder dispensing platforms include a user software package that contains an adaptive learning algorithm to dispense and weigh powders.q For some powders, several dispenses were necessary for optimizing the learning algorithm. The range of % error values was greater for sodium chloride (open bench/SV), D-mannitol (open bench/classic plastic), and limestone powder (glovebox/SV) relative to other powder/equipment environment/ dispense head combinations. Unchained Laboratories Junior (Formerly Freeslate Protégé). The data from 5957 (99.3%) dispenses on the Unchained Laboratories Junior system were retained for analysis. The mass dispensed on the Unchained Laboratories Junior were analyzed for % error as shown in Figure 4.r As the target mass values increased from 2 to 10 to 50 mg, the % error values improved for all powders. As previously discussed, Unchained Laboratories powder dispensing platforms include a user software package that contains an adaptive learning algorithm to dispense and weigh powders.q For some powders, several dispenses were necessary for optimizing the learning algorithm, for example limestone powder/classic metal head and thiamine HCl/classic plastic head/10 mg. The range of % error values was more significant for D-mannitol/classic metal head, limestone powder/classic metal head, and sodium chloride/classic metal head or SV head. All dispenses on the Unchained Laboratories Junior system were performed in an LEV environment. Impact of Dispense Time. The primary focus of this evaluation was to identify how accurately systems dispensed the range of selected powders and target masses. However, dispensing efficiency is also crucial to successfully running high-throughput experiments. During this evaluation, the working group collected dispense times from each system where it was possible. 1504



Article

Figure 5. Mass error vs dispense time for all platforms. Scatterplot of % error values (−25% to +1000%) vs dispense time (0−500 s), broken down by powder (columns), target mass in mg (rows), and platform (colors).

used; otherwise the dispense head had little impact on the dispense time.

Dispense time data and mass dispensed data from each automated powder dispensing platform were aggregated by powder, target mass, and dispense head, and then quantified by calculating mean and % RSD mass values for each subset.s Figure 5 shows a plot comparing the % error vs dispense time values for retained dispenses on each platform.t Of the 2776 dispenses on the Chemspeed SWING systems, 21.4% had dispense time data. Dispense time data were unable to be captured from two of the three installed systems. Dispense times increased as the target mass increased from 2 to 10 mg but were relatively consistent between 10 and 50 mg.u Dispense times were longer in general for D-mannitol than for the other powders. Of the 3692 dispenses on the Mettler-Toledo Quantos QB5 systems, dispense time data were only recorded from two of the three installed systems. The equipment environment had little impact on the dispense time. The dispense times increased for all powders as the target mass increased from 2 to 10 to 50 mg.v Fumed silica had longer dispense times relative to the other six powders, whose dispense times were relatively consistent across all target masses. The Unchained Laboratories Freeslate systems collected time data for all dispenses. The powder being dispensed, the dispense head being used, the equipment location, and the target mass all influenced the dispense time.w Dispense times were longer and % errors higher for D-mannitol, PVPP, and thiamine HCl in an open bench environment when using classic plastic dispense heads vs SV head. The Unchained Laboratories Junior system collected time data for all dispenses. The powder being dispensed, the dispense head being used, and the target mass all had an effect on the dispense time.x Fumed silica had longer dispense times relative to the other six powders. Dispense times were longer for thiamine HCl when the classic plastic dispense head was

■

SUMMARY Each system was capable of dispensing powders with comparable mean mass values.y The powder dispensing data included in this evaluation demonstrate the general utility of the Mettler-Toledo Quantos QB5 and QH012-LNMP dispense head for all powder/target mass combinations evaluated in this study. Optimal dispense head selection was essential for best performance for both the Chemspeed SWING and Unchained Laboratories systems. On the Chemspeed SWING systems, the smaller thread size of the type 1 and type 1D dispense heads provided greater control over dispensing of powders with better flow properties, for example sodium chloride, thiamine HCl, and D-mannitol. On the Unchained Laboratories Freeslate system, the equipment environment impacted powder dispensing performance. Lproline and sodium chloride dispenses using an SV head in a glovebox environment performed better than those in an open bench environment. On the Unchained Laboratories Junior system, the less dense/smaller size powders L-proline, limestone powder, PVPP, and thiamine HCl were easier to dispense with the SV head than the denser/larger size powders D-mannitol and sodium chloride.

■

CONCLUSIONS The powder being dispensed and the target mass had an impact on the accuracy of the mass dispensed and dispense time on all platforms. The less dense/smaller size organic powders L-proline and thiamine HCl dispensed better overall than the more dense/larger size organic powder D-mannitol. The polymeric powder PVPP dispensed well generally across all platforms. The inorganic powders fumed silica, limestone powder, and sodium chloride were the most challenging to 1505



Article

systems (e.g., electronic laboratory notebooks, HTE liquid handling platforms and analytical systems) will become essential. Productivity gains could be realized by the ability of future automated powder dispensing systems to integrate with other HTE systems. This type of software and hardwareintegrated HTE system would require the right balance of simplicity for robustness and automation for throughput. Data should be seamlessly transferred throughout the ecosystem, from an electronic lab notebook through the HTE system to statistical, kinetic, or machine learning softwares. Plates of powder and liquids could be transferred between components automatically or manually where human intervention increases robustness or decreases overall system size and complexity. We hope this study prompts further developments in automated powder dispensing technologies and we look forward to their implementation in pharmaceutical R&D activities.

dispense. Dispenses with a 2 mg target mass had on average 190% to 680% higher % RSD mass values than dispenses with a 10 mg target mass, and 260% to 1700% higher % RSD mass values than dispenses with a 50 mg target mass. This reveals a gap in the ability for current systems to accurately dispense low target masses. This study resulted in the identification of strengths and gaps for each automated powder dispensing platform. The Chemspeed SWING/SDU systems generally had the shortest dispense times, and the SDU technology was well-suited for dispensing the dense, large size, fast flowing powder sodium chloride. The Chemspeed SWING/SDU systems experienced a large percentage of machine stalls, ending runs before their desired conclusion and thus requiring additional time and intervention on the part of the researcher. The Mettler-Toledo Quantos QB5 systems using the QH012-LNMP dispense head had a balanced combination of low % error, low % RSD mass, and low dispense time values across all seven powders and multiple equipment environments. The Mettler-Toledo Quantos QB5 systems use a carousel layout, and they are limited to one-to-30 powder dispensing only, in contrast to the Chemspeed and Unchained Laboratories systems, which each had many-to-many and 96 well plate powder dispensing capabilities integrated with liquid handling systems. The Unchained Laboratories systems could deliver a balanced combination of low % error and low % RSD mass values across all seven powders and benefited from an integrated software package that facilitated data transfer and capture from design to execution and reporting. The Unchained Laboratories systems were very sensitive to dispense head selection and equipment environment, requiring researchers to have a sufficient knowledge base to achieve optimal results for a given powder. The strengths of these four automated powder dispensing platforms have allowed pharmaceutical researchers to develop HTE workflows that have advanced R&D strategies and led to increased expectations for project throughput, data complexity, and research productivity. These increasing expectations make the gaps of these systems more impactful and point to opportunities for improved technologies to meet these productivity demands. Future Directions. While no system gave perfect results, the authors hope that this study will be helpful to fellow researchers, in their uses of this equipment, and to all vendors of automated powder dispensing systems to help rectify gaps in current offerings. As the HTE WG of the ETC was conducting these experiments and preparing this manuscript, newer and more updated HTE powder dispensing platforms became available. The evaluation of these new platforms could not be performed by the HTE WG, but the ETC welcomes manufacturers of those instruments to run their systems through the testing and analysis protocol described here. Powder dispensing technology with improved accuracy and precision for target masses less than 10 mg would expand the utility of automated powder dispensing platforms into additional workflows.z Increasing productivity pressures on the pharmaceutical industry could increase the demand for improved powder dispensing in this low mass range, in order to explore more HTE variables using less material earlier in development programs. Second, data integrity and management are increasingly important issues for researchers in pharmaceutical R&D. Improving data capture and integration between automated powder dispensing platforms and other

■

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.oprd.8b00259. Automated powder dispensing platform specifications; Depictions of powder dispensing systems; Run order schedule; Dispense head refill schedules; Powder characterization tests; PCA summary plots and loading plot; Tables of discounted dispenses; Tables of the mean, median and % RSD values for mass dispensed and dispense time for all retained dispenses on each platform; Tables of mean mass and % RSD mass values from dispenses on the Chemspeed SWING, MettlerToledo Quantos, and Unchained Laboratories Junior systems (PDF)

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel.: +1 610 270 4734. ORCID

Matthew N. Bahr: 0000-0003-4427-5156 Present Address ▲

S.C.: SRI International, 201 Washington Road, Princeton, New Jersey 08540, United States

Author Contributions ■

(M.N.B., D.B.D.) These authors contributed equally. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS

The ETC High Throughput Experimentation WG would like to thank the ETC Secretariat and ETC Board for providing the forum and guidance for this work to be accomplished, and in addition we thank Klaus Dress, Margaret Faul, Bruno Hancock, Joel Hawkins, David Juboor, Kris Jones, William Ketterhagen, Alexis Myers, Ka Nip Ying, Timothy Rhodes, Jacob St. Germain, Yui Tateno, James Vergis, Kenneth Wells, and Weili Yu for their specific contributions. 1506



■

Article

The % error range for all 5,957 dispenses was −10% to +3,245%. 95.3% of all dispenses were between −25% to +100% error. 278 dispenses had greater than +100% error. Not every powder/dispense head combination was tested. s The Supporting Information section includes complete data tables of the mean, median and % RSD values for mass dispensed and dispense time for all retained dispenses on each platform. t The Supporting Information section includes additional scatterplots comparing % error values vs dispense times for each platform. u The dispense times on the Chemspeed SWING system ranged from 13 to 65 s. v The dispense times on the Mettler-Toledo Quantos systems ranged from 10 to 781 s. One dispense time was greater than 500 s. w The dispense times on the Unchained Laboratories Freeslate systems ranged from 16 to 3761 s. 163 dispense times were greater than 500 s. 95.7% of all dispense times were between 0 and 500 s. x The dispense times on the Unchained Laboratories Junior system ranged from 13 to 3055 s. 97 dispense times were greater than 500 s. 98.4% of all dispense times were between 0 and 500 s. y The Supporting Information section includes data tables comparing mean mass and % RSD mass values from dispenses on the Chemspeed SWING and Mettler-Toledo Quantos in a purgebox environment, and dispenses from the Mettler-Toledo Quantos and Unchained Laboratories Junior in an LEV environment. z This gap in low mg powder dispenses is currently filled by solution dispensing followed by evaporation, a less efficient method than powder dispensing and not always doable. r

ABBREVIATIONS API, active pharmaceutical ingredient; ETC, Enabling Technologies Consortium; HTE, high-throughput experimentation; RH, relative humidity; WG, working group

■

ADDITIONAL NOTES Visit www.etconsortium.org for more information. b The authors met with each manufacturer throughout the testing, and prior to this publication to review the data. The initial results of this study were presented at the Pittcon Conference on 01March2018 (www.pittcon.org). c TIBCO Spotfire version 7.0.2.8 was used. d Microsoft Excel 2010 version 14.0.7195.5000 SP2MSO was used. e Ten to 50 mg span the range typically used in our current HTE workflows. Two mg was included to test the performance of systems at a low-end mass level. f The Chemspeed and Unchained Laboratories systems had all runs conducted in a single experiment. The Mettler-Toledo systems had each run conducted individually. g This number of dispenses was chosen to allow systems adequate opportunity to learn and optimize dispensing parameters for each new powder and target mass, and to generate a sufficient number of replicates for analysis. h The Run Order Schedule may be found in the Supporting Information section. i The Supporting Information section includes data tables of discounted and retained dispenses from each platform. j Attempts to dispense fumed silica on the Chemspeed SWING and Unchained Laboratories Freeslate systems were unsuccessful. k The % error range for all 2,776 dispenses was −55% to +355%. 97.0% of all dispenses were between −25% to +100% error. 66 dispenses had less than −25% error and 18 dispenses had greater than +100% error. Not every powder/dispense head combination was tested. l “Teaching” is an optional operation in the Chemspeed SWING in which the robot will conduct three practice dispenses and use the results to set the dispensing parameters for subsequent dispenses. Teaching typically requires about 5 min to complete. m The % error range for all 3,692 dispenses was −98% to +307%. 99.7% of all dispenses were between −25% to +100% error. One dispense had less than −25% error and 9 dispenses had greater than +100% error. Not every powder/equipment environment combination was tested. n The learning algorithm is complete typically after the initial several (generally 1−3) vials of a run. Each of these vials may require additional time (generally 30−90 s) for the dispense to complete compared to vials later in the run. o This system is a vintage model that was set to weigh at ∓% tolerance. p The % error range for all 3,750 dispenses was −80% to +15,960%. 97.4% of all dispenses were between −25% to +100% error. Sixteen dispenses had less than −25% error and 83 dispenses had greater than +100% error. Not every powder/ dispense head/equipment environment was tested. q The learning algorithm is complete typically after the initial several (generally 1−3) vials of a run. Each of these vials may require additional time (generally 30−90 s) for the dispense to complete compared to vials later in the run. a

■

REFERENCES

(1) (a) Welch, C. J.; Hawkins, J. M.; Tom, J. Precompetitive Collaboration on Enabling Technologies for the Pharmaceutical Industry. Org. Process Res. Dev. 2014, 18 (4), 481−487. (b) Leil, T. A.; Bertz, R. Quantitative Systems Pharmacology can reduce attrition and improve productivity in pharmaceutical research and development. Front. Pharmacol. 2014, 5 (247), 1−6. (c) Paul, S. M.; Mytelka, D. S.; Dunwiddie, C. T.; Persinger, C. C.; Munos, B. H.; Lindborg, S. R.; Schacht, A. L. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discovery 2010, 9, 203− 214. (2) (a) Selekman, J. A.; Qiu, J.; Tran, K.; Stevens, J.; Rosso, V.; Simmons, E.; Xiao, Y.; Janey, J. High-Throughput Automation in Chemical Process Development. Annu. Rev. Chem. Biomol. Eng. 2017, 8 (1), 525−547. (b) Krska, S. W.; DiRocco, D. A.; Dreher, S. D.; Shevlin, M. The Evolution of Chemical High-Throughput Experimentation To Address Challenging Problems in Pharmaceutical Synthesis. Acc. Chem. Res. 2017, 50 (12), 2976−2985. (c) Fridgeirsdottir, G. A.; Harris, R.; Fischer, P. M.; Roberts, C. J. Support Tools in Formulation Development for Poorly Soluble Drugs. J. Pharm. Sci. 2016, 105 (8), 2260−2269. (d) Petruševska, M.; Horvat, M.; Peternel, L.; Kristan, K. High-throughput automated dissolution method applicable for a wide dose range of controlled release pellets. Drug Dev. Ind. Pharm. 2016, 42 (7), 1149−1157. (e) Caschera, F.; Gazzola, G.; Bedau, M. A.; Bosch Moreno, C.; Buchanan, A.; Cawse, J.; Packard, N.; Hanczyc, M. M. Automated Discovery of Novel Drug Formulations Using Predictive Iterated High Throughput Experimentation. PLoS One 2010, 5 (1), e8546. (3) (a) Rubin, A. E.; Tummala, S.; Both, D. A.; Wang, C.; Delaney, E. J. Emerging Technologies Supporting Chemical Process R&D and Their Increasing Impact on Productivity in the Pharmaceutical 1507



Article

Industry. Chem. Rev. 2006, 106 (7), 2794−2810. (b) Nunn, C.; DiPietro, A.; Hodnett, N.; Sun, P.; Wells, K. M. High-Throughput Automated Design of Experiment (DoE) and Kinetic Modeling to Aid in Process Development of an API. Org. Process Res. Dev. 2018, 22 (1), 54−61. (4) (a) Newman, A. Specialized Solid Form Screening Techniques. Org. Process Res. Dev. 2013, 17 (3), 457−471. (b) Aaltonen, J.; Allesø, M.; Mirza, S.; Koradia, V.; Gordon, K.; Rantanen, J. Solid form screening - A review. Eur. J. Pharm. Biopharm. 2009, 71, 23−37. (c) Wyttenbach, N.; Sutter, B.; Hidber, P. High-Throughput Strategies and Techniques for Salt Screening. In Pharmaceutical Salts: Properties, Selection, and Use, 2nd ed.; Stahl, P. H., Ed.; Verlag Helvetica Chimica Acta: Zürich, Switzerland, 2011; pp 203−233. (d) Morrison, H.; Mrozek-Morrison, M.; Toschi, J.; Luu, V.; Tan, H.; Daurio, D. High Throughput Bench-Top Co-crystal Screening via a Floating Foam Rack/Sonic Bath Method. Org. Process Res. Dev. 2013, 17 (3), 533−539. (e) Variankaval, N.; McNevin, M.; Shultz, S.; Trzaska, S. High-Throughput Screening to Enable Salt and Polymorph Screening, Chemical Purification, and Chiral Resolution A2. In Comprehensive Organic Synthesis II, 2nd ed.; Molander, G. A., Ed.; Elsevier: Amsterdam, 2014; pp 207−233. (f) Selekman, J. A.; Roberts, D.; Rosso, V.; Qiu, J.; Nolfo, J.; Gao, Q.; Janey, J. Development of a Highly Automated Workflow for Investigating Polymorphism and Assessing Risk of Forming Undesired Crystal Forms within a Crystallization Design Space. Org. Process Res. Dev. 2016, 20 (1), 70−75. (5) (a) Thygs, F. B.; Merz, J.; Schembecker, G. Automation of Solubility Measurements on a Robotic Platform. Chem. Eng. Technol. 2016, 39 (6), 1049−1057. (b) Alsenz, J.; Kansy, M. High throughput solubility measurement in drug discovery and development. Adv. Drug Delivery Rev. 2007, 59 (7), 546−567. (6) (a) Maddux, N. R.; Iyer, V.; Cheng, W.; Youssef, A. M. K.; Joshi, S. B.; Volkin, D. B.; Ralston, J. P.; Winter, G.; Middaugh, C. R. High Throughput Prediction of the Long-Term Stability of Pharmaceutical Macromolecules from Short-Term Multi-Instrument Spectroscopic Data. J. Pharm. Sci. 2014, 103 (3), 828−839. (b) Chelius, D.; Xiao, G.; Nichols, A. C.; Vizel, A.; He, B.; Dillon, T. M.; Rehder, D. S.; Pipes, G. D.; Kraft, E.; Oroska, A.; Treuheit, M. J.; Bondarenko, P. V. Automated tryptic digestion procedure for HPLC/MS/MS peptide mapping of immunoglobulin gamma antibodies in pharmaceutics. J. Pharm. Biomed. Anal. 2008, 47 (2), 285−294. (c) Palermo, P. J., Solid Dosage-Form Analysis. In Sep. Sci. Technol., Ahuja, S., Scypinski, S., Eds. Academic Press: San Diego, 2001; Vol. 3, pp 235−267. (7) (a) Hartford, G.; Cheung, P.; Whitaker, K.; Helmy, R.; Ratcliff, J. Effective and Efficient Weighing of Potent Compounds. Pharmaceutical Technology 2013, 37 (9), 42−56. (b) Fermier, A. M.; Troisi, J.; Heritage, E. C.; Drexel, M. A.; Gallea, P.; Swinney, K. A. Powder dispensing robot for sample preparation. Analyst 2003, 128 (6), 790− 795. (8) Welch, C. J.; Faul, M. M.; Tummala, S.; Papageorgiou, C. D.; Hicks, F.; Hawkins, J. M.; Thomson, N.; Cote, A.; Bordawekar, S.; Wittenberger, S. J.; Laffan, D.; Purdie, M.; Boulas, P.; Irdam, E.; Horspool, K.; Yang, B.-S.; Tom, J.; Fernandez, P.; Ferretti, A.; May, S.; Seibert, K.; Wells, K.; McKeown, R. The Enabling Technologies Consortium (ETC): Fostering Precompetitive Collaborations on New Enabling Technologies for Pharmaceutical Research and Development. Org. Process Res. Dev. 2017, 21 (3), 414−419.

1508


Collaborative Evaluation of Commercially Available Automated

Recommend Documents