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The AAPS Journal

, Volume 10, Issue 2, pp 282–288 | Cite as

Current Methods for Predicting Human Food Effect

  • Kimberley A. LentzEmail author
Open Access
Article

Abstract

Food can impact the pharmacokinetics of a drug product through several mechanisms, including but not limited to, enhancement in drug solubility, changes in GI physiology, or direct interaction with the drug. Significant food effects complicate development of new drugs, especially when clinical plans require control and/or monitoring of food intake in relation to dosing. The prediction of whether a drug or drug product will show a human food effect is challenging. In vitro models which consider physical–chemical properties can classify the potential for a compound to demonstrate a positive, negative or no food effect, and may be appropriate for screening compounds at early stages of drug discovery. When comparing various formulations, dissolution tests in biorelevant media can serve as a predictor of human drug performance under fasted and fed conditions. Few in vivo models exist which predict the magnitude of change in pharmacokinetic parameters in humans when dosing in the presence of food, with the dog appearing to be the most studied species for this purpose. Control of gastric pH, as well as the amount and composition of the fed state in dogs are critical parameters to improving the predictability of the dog overall as a food effect model. No single universal model is applicable for all drugs at all stages of drug development. One or more models may be required depending whether the goal is to assess potential for a food effect, determine the magnitude of change in pharmacokinetic parameters in the fed/fasted state, or whether formulation efforts have the ability to mitigate an observed food effect.

Key words

BCS food effect in vitro in vivo model prediction 

INTRODUCTION

Food can impact the pharmacokinetics of a drug product through several mechanisms, such as delay in gastric emptying, stimulation of bile flow, changes in gastrointestinal (GI) pH, alterations in luminal metabolism, or interactions of the drug with the food itself (1,2). The drug absorption process can be affected by many factors, including calorie content (low vs high calorie meals), nutrient composition (protein, carbohydrate-rich or high-fat meals), volume, temperature of the meal itself, and fluid ingestion. Food also increases blood flow to the liver (splanchnic blood flow); therefore, changes in first pass extraction that occur as a result, may cause differences in bioavailability between the fed and fasted state. For compounds with saturable first pass extraction, the bioavailability will increase with food intake; whereas the opposite will occur if hepatic enzymes are not saturated during first pass. Propranolol, metoprolol and propafenone are examples of drugs with high first-pass metabolism whose pharmacokinetics are impacted by food (3). Michael Camilleri of the Mayo Clinic published a thorough review on the topic of changes in human GI physiology with food intake (4). This reference provides significantly more detail on many of the mechanisms discussed above.

In addition to physiologic considerations, factors such as non-specific binding, sequestration, or chemical instability can cause drug–food interactions. If a drug chelates with ions present in the ingested meal, drug dissolution and/or absorption may be reduced. The meal itself may pose a physical barrier that prevents drug diffusion to the site of absorption, likewise resulting in decreased bioavailability. Drug instability as a result of acid degradation may be exacerbated by prolonged gastric residence after food ingestion.

For particularly lipophilic drugs or large molecular weight macromolecules, lymphatic uptake can be increased by the presence of a high-fat meal, thereby lowering plasma drug levels (5,6). Gershkovich and Hoffman suggest that changes in drug disposition for certain lipophilic compounds may occur when the drug interacts with triglyceride-rich lipoproteins (TRL), which elevate as a result of consumption of high fat meals (7). This work proposes that various drugs bind to TRL at different sites in vivo. For example, if this occurs within the enterocyte, lymphatic uptake is likely and first pass metabolism is by-passed (8). For other lipophilic compounds, a sequestration of drug by TRL takes place in the plasma after the absorption process is complete, resulting in a decrease in parameters such as volume of distribution and clearance. In these cases, the observed food effect is better mechanistically explained by postprandial hyperlipidemia.

The United States Food and Drug Administration (FDA) recognized the potential for food to alter the pharmacokinetics of drug products and established standards for the design of clinical food effect studies. In the FDA Guidance on Food-Effect Bioavailability and Fed Bioequivalence Studies, it is recommended that drug products are administered under fasted and fed conditions, where the fed state consists of a high fat meal (1,000 calories; 50% from fat) to maximize the potential for an observable food effect (9). If the 90% confidence interval (CI) for the population geometric means between fed and fasted treatment for C max and AUC do not meet the 80–125% bioequivalence (BE) criterion, a food effect is demonstrated, and the potential for clinical significance of the food effect should be considered.

Several challenges exist in the development of compounds that exhibit food effects. If a high fat meal is required to obtain efficacious drug levels, there is potential for sub-therapeutic dosing in patients taking the drug without food. For compounds with a narrow therapeutic index, changes in bioavailability, particularly in the positive direction, may produce unwanted side effects. As a result, the clinical plan may require control and/or monitoring of food intake in relation to dosing.

Competition with other drugs which do not exhibit a food effect may even offer a commercial advantage. Auiler et al. compared the effect of food on the exposure of an extended release methylphenidate formulation (Concerta®) to that of an extended release amphetamine formulation (Adderall XR®). Both compounds are used for the treatment of attention-deficit/hyperactivity disorder (ADHD). In this study, the early plasma concentrations of amphetamine from the Adderall XR® formulation were lower in patients when the drug was administered with a high fat breakfast. However, drug exposure for methylphenidate from the Concerta® formulation was not affected by food. The authors concluded less variability in drug exposure in patients taking Concerta® compared to Adderall XR® particularly if meal intake varied (10). Although these results were challenged because the study compared partial area AUCs as opposed to traditional BE criteria, it illustrates how food effect data can also be used to promote market advantage between drugs in a therapeutic class (11).

Despite the implications outlined above, the prediction of whether a drug or drug product will show a human food effect is challenging, and no one model is sufficiently comprehensive to accurately predict food effect in all cases. Depending on the particular stage of drug development, the rationale for determining food effect may be different, thus influencing the choice of which model is most appropriate. The present work reviews the current methods for the prediction of human food effect and compares the advantages and disadvantages of various in vitro and in vivo approaches.

IN SILICO METHODS FOR PREDICTING FOOD EFFECT

Singh compared whether aqueous solubility, dose/solubility ratio and Log P could be correlated to human fed/fasted AUC through the analysis of over 100 structurally diverse compounds (12). Aqueous solubility and lipophilicity played a “predominant role” in anticipating human food effect. Consideration of the drug’s dose in relation to its solubility (dose/solubility ratio) further improved predictability as compared to solubility alone. This work concluded that although food effect was able to be correlated to these physicochemical properties, it was more difficult to make quantitative prediction of the magnitude of AUC change between fasted and fed treatments. Other biopharmaceutic properties, such as permeability, were not considered.

Wu and Benet suggest food effect can be anticipated on the basis of both solubility and permeability as described by the Biopharmaceutics Classification System (BCS) (13,14). Figure 1 summarizes the effect of food on the direction of change in extent of drug exposure (AUC) as a function of BCS class. In general, BCS Class I compounds are least affected by food. Those belonging to BCS Class II (low solubility, high permeability) are more likely to show a positive food effect, due to increased in vivo solubility in the fed state. It is generally recognized that not all BCS Class II compounds should be grouped together. Rather, the category can be subdivided into weak acids, weak bases, and lipophilic compounds. Each will respond differently to food effect. For weak acids and weak bases with high pK as (a majority of compounds in this class), meals which stimulate bile flow and enzymatic activity enhance dissolution and subsequently result in positive food effects. However, it is possible for a compound with a low pK a to actually precipitate in the stomach or upper small intestine if the pH of the GI contents is elevated due to meal ingestion. In those instances, bioavailability could actually decrease for a BCS Class II compound. BCS Class III (high solubility, low permeability) drugs tend to have negative food effects, particularly if food interacts with drug absorption. For BCS Class IV compounds, it is more difficult to anticipate the direction of the change in AUC. This could be because some BCS Class IV compounds have been “misclassified” based on strict definition of the in vitro criterion of the BCS. When these types of compounds are dosed, their ability to perform in vivo like BCS Class II or III compounds will influence how food may impact AUC. If the poor in vitro solubility of a BCS Class IV compound is improved in vivo (possibly through formulation efforts) it may behave like a BCS Class III compound. Other Class IV compounds may have in vitro intestinal permeability just below a suitable high permeable reference compound in vitro, yet in vivo may be well absorbed throughout the entire GI tract, or be a substrate for an uptake transporter. Those would behave like a BCS Class II compound, and show increases in AUC in the presence of food. More challenging are those Class IV compounds with both altered in vivo dissolution and a narrow absorption window in the GI tract, which make it difficult to anticipate the direction of food effect.
Fig. 1

The effect of food on the direction of change in extent of drug exposure (AUC) as a function of BCS class

Gu et al. compared BCS Class to the observed human food effect of 92 sets of clinical data (15). This analysis revealed that 67% of BCS Class I compounds in the data set showed no food effect, 71% of the BCS Class II compounds had a positive food effect, and 61% of BCS Class III compounds showed a negative food effect. Of the Class IV compounds in this dataset, a large majority (73%) showed a positive food effect. To improve predictability, the authors generated a statistical model which incorporated maximum absorbable dose (MAD), dose number and LogD. For the 92 compounds in this dataset, the probability of correctly predicting the category of food effect (positive, negative, or no food effect) was 80%. However, there was a substantial difference in accuracy of prediction depending upon the direction of the food effect. The correct predictions were 97% for positive food effects, 79% for negative food effects and 68% for no food effects.

These methods classify compounds as likely to show a positive, negative, or no food effect. The advantage to these approaches is that parameters such as solubility and permeability are relatively easy to determine with minimal amount of drug substance. Therefore, at early stages of drug discovery, it is possible to screen many compounds for food effect “potential”. Unfortunately, these particular methods are less accurate at predicting the magnitude in the change in pharmacokinetic parameters (such as C max or AUC) in the presence of food. It is also not possible to compare several different formulations, and provides no mechanistic understanding as to the cause of the food effect.

More recent work in the modelling and simulations area has demonstrated the possibility of predicting a compound’s pharmacokinetic profile in the fed and fasted state, using commercially available software packages. Jones et al. simulated the impact of food on the pharmacokinetics of several Roche compounds (16). These authors developed physiologically based pharmacokinetic models (PBPK) in GastroPlus® using permeability, solubility, metabolism and distribution data for each compound. Incorporation of physiological parameters, in combination with drug solubility data in various biorelevant medias, allowed for the oral pharmacokinetics of each drug to be simulated under fasted and fed conditions. For the six dissolution rate limited compounds studied in this work, the simulations were able to capture the magnitude of the observed human food effect. Although a detailed discussion of PBPK modelling is beyond the scope of this work, it is a powerful tool for predicting human pharmacokinetic profiles throughout the various stages of drug discovery and development (17).

BIORELEVANT DISSOLUTION

Several groups have reported on the use of dissolution in biorelevant media as a predictor of drug performance in humans under fasted and fed conditions. Specialty dissolution medias have included milk, fasted simulated small intestinal fluid (FaSSIF), fed simulated small intestinal fluid (FeSSIF), and/or modifications to these medias in phospholipid or bile salt content, pH, or inclusion of lipolytic enzymes (18, 19, 20).

Food effects on drug absorption are generally better predicted when using biorelevant media containing bile salts and lecithin as compared to the traditional USP compendial media such as simulated gastric fluid (SGF) and simulated intestinal fluids (SIF). It is, however, difficult to design a universal simulated fed state media for use with all compounds because the composition of in vivo fed state fluids is highly dependent on the ingested meal itself. The concentration of bile salt and lecithin in standard FeSSIF is based on average peak bile output and therefore, may not be appropriate in cases where drugs are dosed at various times after meal ingestion as compared to when they are co-administered with food. Nicolaides et al. recommends starting with milk and adding components such as pepsin or lipase to improve correlations when working with specific compounds (18).

Dissolution data for several BCS Class II compounds (danazol, ketoconazole, atovaquone, and troglitazone) was shown to correlate well to observed human fed and fasted pharmacokinetic data (20). For danazol, dissolution profiles in standard SIF were incomplete, but use of simulated fasted and fed intestinal media was able to suggest a food effect was anticipated. This work also showed the importance of volume adjustment in dissolution tests such that more physiologic volumes were considered.

Biorelevant dissolution tests offer several advantages. They are considered more physiologically appropriate than standard compendial USP dissolution medias, and examples exist where their use can allow for the anticipation of bioavailability improvement in the fed state. In some cases, this technique has been shown to predict fed state pharmacokinetic profiles for lipophilic drugs in humans. Biorelevant dissolution tests can be utilized to compare several prototype formulations in vitro, to select a best one prior to the investment of human clinical testing. One disadvantage is that dissolution tests require a formal formulation, thus this technique may not be as helpful to early preclinical drug development. Additionally, composition of the media may require optimization to improve predictability; therefore a “one size fits all” approach is unlikely to work for all compounds. For this optimization to occur, actual human data must be available, which implies a compound has already progressed into clinical development.

IN VIVO METHODS

Although certain physiological differences in gastric pH, gastric emptying and intestinal transit time exist between dogs and humans, the dog is the most studied species for understanding or predicting human food effect (21, 22, 23, 24, 25). Fewer reports of food effect studies have been conducted in species such as rat, monkey, or mini-pig. The size of the rat limits the ability to dose traditional dosage forms, such as capsules and tablets, although when a drug can be administered as a liquid, the rat can be used. In addition, rats do not possess a gallbladder, thus secretion of bile to the duodenum is a continuous process. This differs to humans, where bile secretion is stimulated by the presence of food. The overall volume of rat GI fluid is also low in relation to other species. Monkeys may be a commonly used species for pharmaceutical testing; yet, there are few papers which report the utility of this species for food effect studies. Kondo et al. showed the gastric pH after feeding of a standard biscuit-type meal to be higher in cynomolgus monkeys than the fed state pH in humans (26). Thus, optimization of the fed state test meal may be required before the monkey could yield food effect data similar to the human. In a recent paper by Grove et al., the mini-pig was used to investigate the effect of food on several lipid-based formulations of seocalcitol, a poorly soluble compound (27). Although the biliary system and pancreatic duct in minipigs and humans are more physiologically comparable (24,28), the overall use of this species as a food effect model is limited throughout the literature. The advantage of the dog is that it is a more appropriate model for dosing solid oral dosage forms. When interested in considering formulation effects, as well as the impact of the drug itself, dogs are a better preclinical species for this work.

In the dog, the amount and composition of the fed state test meal varies substantially. Table I summarizes the type of test meal, amount, and outcome or purpose of several food effect studies performed the dog. Some studies were initiated because a food effect was observed clinically in humans, and the dog studies were designed retrospectively to determine if the food effect would have been predicted (5,29,30). Others used the dog under varying fed state conditions to optimize formulation strategies (31, 32, 33, 34).
Table I

Composition and Amount of Test Meal Administered to Dogs in the Fed State of Various Food Effect Studies

Meal composition

Amount

Compound

Result

Ref.

Ensure®

240 mL of each test meal administered as liquid diet

Anti-fungal agent

A 75% reduction in AUC was first observed in humans administered a high fat diet. Carbohydrate meals in dogs did not cause a negative effect, whereas protein and lipid meals in dogs produced a negative food effects. Proposed mechanisms for cause of negative food effect are presented

(5)

Intralipid®

CASEC®

Sucrose

Moducal®

Standard dog food

600 g 2.5% fat 7.5% protein

Halofantrine

A 12-fold increase in bioavailability was observed in fed dogs, compared to a 3–5 fold increase in fed humans.

(29)

Human meal homogenates (low, medium, high fat)

1 full meal (amount of high fat meal administered to dog was the same as that in human)

Celecoxib

Dog overpredicted the magnitude of the observed human food effect.

(30)

Commercial solid food

200 g

R1315 (BCS Class II)

Presented a strategy for Phase I formulation development where drug absorption in humans was simulated in silico using GastroPlus™ and compared with the results of prototype formulations in fed and fasted dogs.

(31)

Commercial solid food

100 g

Fluorescein

Study compared effect of food on the PK of various formulations (Colon delivery capsules, standard gelatin capsules and enteric capsules). No comparative human data presented

(32)

Alpo “Chunky Beef for Dogs”

374 g 55% calories from fat

NK1 antagonist (MK-0869)

Dog utilized in fed state for comparison of various formulation strategies. Fed/fasted human data with nanoparticulate formulation well correlated to dog data

(33)

FDA high fat breakfast

1 full meal (amount of high fat meal administered to dog was the same as that in human)

Theophylline

Rate and extent of absorption in dogs was similar to humans under fasting conditions. Good qualitative agreement between dog and human data for different formulations of theophylline administered in the fed state

(34)

Milk

690 mL (3.5% fat) 440 kcal

l-sulpiride

C max, AUC, and %F higher in mongrel dogs given a 100 mg tablet than those in humans also fed a 440 kcal diet and dosed with a 100 mg tablet. l-sulpiride is a poorly permeable compound

(36)

FDA high fat breakfast

50 or 100 g aliquot; pentagastrin pretreatment in both the fed and fasted state

Various (see Table II)

50 g aliquot in dogs provided a better correlation to human fed/fasted C max and AUC. 100 g aliquot overpredicted observed human fed/fasted data. Use of pentagastrin important for improving overall predictability of the model

(35)

Wu et al. performed a series of experiments using the dog to evaluate prototype formulations of MK-0869, an NK1 antagonist (33). This compound entered early clinical development, where a food effect was observed in human subjects receiving a traditional tablet formulation of this BCS Class II compound. New formulations intended to increase in vivo dissolution through particle size reduction were designed and administered to beagle dogs, where the fed state consisted of 374 g of Alpo dog food. The dog was used to both optimize the formulation in terms of particle size reduction, as well as to guide the method at which nanoparticles were incorporated into a more traditional solid dosage form. For this compound, the data obtained in the dog was in very good agreement with human data.

Dogs were also found to be well suited for studying the absorption and food effect of different theophylline formulations (34). Despite known GI differences between the dog and human, the dog accurately predicted the rate and extent of theophylline absorption from several theophylline formulations in humans. When dogs were administered an entire human high fat meal, the results were in good agreement with those observed in human subjects for each formulation studied.

In an effort to design a predictive food effect model, a variety of BCS Class I–IV compounds were evaluated in pentagastrin pretreated dogs, administered a 50 g aliquot of FDA high fat meal (35). This model predicts changes in human C max and AUC in the presence of food, which are reflective of the magnitude of change in these parameters observed in humans. Table II illustrates that the fed/fasted C max and AUC in the dog using this set of optimized experimental conditions were in fairly close qualitative agreement with those observed in humans. The amount of test meal (50 vs 100 g), as well as control of gastric pH with pentagastrin, were important parameters to aligning fed/fasted C max and AUC ratios to those observed in humans across a series of diverse compounds. This model can accommodate various formulation types (from suspension in capsule to formulated product), and is particularly useful at the preclinical drug development phase for determining whether a new drug substance may be subject to a food effect.
Table II

Comparison of Fed/Fasted Pharmacokinetic Parameters in the Dog vs Human for Compounds with Various Propensities for Food Effect (35)

Compound

Parameter

Canine fed/fast

Human fed/fast

Atazanavir

AUC

2.7

1.7

C max

2.0

1.6

Celebrex

AUC

2.3

1.2

C max

3.0

1.3

HIV attachment inhibitor

AUC

2.2

2.5

C max

1.7

2.5

Ravuconazole

AUC

5.5

4.0

C max

5.1

4.0

Pravastatin

AUC

0.66

0.69

C max

0.67

0.51

Metformin

AUC

0.85

0.75

C max

0.55

0.60

Aripiprazole

AUC

1.7

1.2

C max

1.0

1.1

Irbesartan

AUC

0.83

1.0

C max

0.69

1.0

Factor Xa inhibitor

AUC

1.4

1.0

C max

1.3

1.1

All dogs pentagastrin pretreated (6 μg/kg) prior to fasted and fed studies. A 50 g aliquot of FDA meal was administered prior to dosing for fed studies.

Although it is easy to administer various formulations and test meals to the dog, this species is not always appropriate for predicting changes in human pharmacokinetics in the presence of food. Fotaki et al. showed the mongrel dog to overestimate absorption of the poorly permeable compound, l-sulpiride (36). In this work, C max, AUC and %F were higher in the dog than those obtained in humans. Loose epithelial junctions in dogs and/or inhibition of intestinal P-glycoprotein by high bile salt levels in fed dogs may explain the disconnect to observed human data, especially for this drug with poor intestinal permeability. In this example, in vitro dissolution data was more useful to predicting human food effect.

It is possible for the dog to over-predict human food effect. Paulson et al. administered celecoxib to dogs receiving low-, medium-, and high-fat high fat human meal homogenates and showed the food effect to be much greater than that observed in humans (30). In this study, the total volume of test meal administered to the dogs was similar to a full human meal and may partly contribute to the over-prediction.

Despite many encouraging pieces of data, dogs are not absolute surrogates for humans in many ways. Martinez et al. have published extensively on known physiologic differences which aid in bridging the gap to explaining interspecies differences in bioavailability (6). In addition to the consideration of differences in presystemic drug metabolism between dog and human, other factors such as diet, formulation, physical–chemical properties of the drug, fluid pH, and GI physiology are important in explaining species-related differences in bioavailability. Failure of the dog to quantitatively predict human food effect may be attributable to these factors. However, it generally appears throughout the literature that control of dog gastric pH and the amount and/or composition of the fed state are important parameters to improving the predictability of the dog overall as a human food effect model.

SOLUBILITY IN INTESTINAL ASPIRATES

Determination of drug solubility in human and canine intestinal contents, obtained in both the fed and fasted state, has recently been reported. This technique should provide a more accurate measure of in vivo drug solubility. Kostewicz et al. showed solubility of danazol, felodipine, and griseofulvin to be greater in dog aspirates than human, mainly due to the higher levels of bile salts present in dog fed state intestinal contents as compared to human (37). This is in contrast to work by Persson et al. which showed some of these same compounds to have similar drug solubility in canine fed intestinal fluid to that determined in fed human intestinal fluid (38). It is noteworthy that the administration of the meal and the methods for obtaining the biological fluids in the dogs and humans differed in the latter study. Here, the test meal used in dog and human consisted of NuTRIflex®, a liquid nutritional drink containing partially metabolized triglycerides and protein. This supplement contains only about 25% of the fat of the standard FDA breakfast.

In another study, Kalantzi et al. administered 500 mL of Ensure® in the dog, prior to collecting dog intestinal aspirates, and determined the solubility of dipyridamole and ketoconazole (39). The results were compared to solubility in human intestinal aspirates obtained under similar meal and collection conditions. These authors showed the bile salt content in dog aspirates to be higher than those obtained in humans, and concluded this difference as the likely cause for the poor agreement in drug solubility between dog and human intestinal fluids.

As was the case with in vivo pharmacokinetic studies in the dog, meal composition and volume appear to influence composition of fed state intestinal fluids in dog, particularly in regard to the amount and nature of the bile salt content of the aspirates. Based on the Persson study, the meal volume and caloric content used in the dog were smaller, and drug solubilities in these aspirates were more closely reflective of human intestinal drug solubility. This suggests that choice of meal administered in the dog may need to be optimized to further improve its correlation to human fed intestinal fluids. The choice of test meal in the dog may strongly influence the composition of canine fed intestinal fluid; therefore, it may or may not be a true reflection of human fed state intestinal medias in all cases.

CONCLUSIONS

In vitro models which consider drug solubility and intestinal permeability are useful for determining if a compound is likely to show a propensity for a food effect in humans. Unfortunately, these models are unable to predict the magnitude of change in fed/fasted parameters such as C max and AUC. Dissolution in biorelevant media has been shown to predict fed state pharmacokinetic profiles for several lipophilic compounds and can allow for the comparison of various formulations prior to in vivo clinical testing. However, volume adjustments or modifications to the composition of the media may need to be optimized for specific compounds. This may require prior knowledge of human clinical data. In vitro dissolution and solubility studies are only useful for food effect prediction if compounds have dissolution or solubility limited absorption, and at best may only be expected to correlate with changes in C max or AUC, not both. Although dogs are the most studied species for predicting human food effect, the composition, amount of the test meal, and control of gastric pH are critical parameters to improving the correlation of data in the dog to those observed in humans. No one universal model is applicable for all drugs at all stages of drug development. One or more models described herein may be required depending whether the goal is to determine potential for a food effect, predict the magnitude of change to pharmacokinetic parameters in the fed/fasted state, or determine whether formulation efforts have the ability to mitigate food effect.

Notes

Acknowledgement

The author would like to thank Kenneth Santone and Punit Marathe for valuable comments and support of this work.

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Copyright information

© American Association of Pharmaceutical Scientists 2008

Authors and Affiliations

  1. 1.Pharmaceutical Candidate Optimization: Metabolism and PharmacokineticsBristol-Myers Squibb Research and DevelopmentWallingfordUSA

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