Pampa Dissolution：An Alternative Method for Oral NDA Formulation Development

 

Joe Chou¹, Roger Lai², Jason Chou¹, Shelly Fu², Wei-Hsuan Wang¹

¹YQ Biotech Ltd., Taiwan.

²Isuzu Optics Ltd., Taiwan.

 

ABSTRACT:

The success of a new drug development relies not only on early-stage drugs screening and preclinical animal studies but also PK/PD prediction prior to clinical study. In drug dosage design, the oral formulation is still the most commonly needed due to its convenience in administration. A number of recent reports in new drugs development have pointed out that PBPK modeling of ADME may lead to better prediction of bioavailability. In order to improve the development of NDA oral formulation, an alternative method using FDA approved PK data base and PAMPA Dissolution is proposed upon “similar PK parameters, similar PK profile” which is believed to potentially shorten the research time and reduce the clinical risk in NDA formulation development.

 

 

 

INTRODUCTION:

The new drugs development has been traditionally conducted with drugs screening, toxicity, efficacy via various cell lines and animsals study prior to clinical study¹. Recent advancement in biotechnology and genomic research has opened another big window in new drugs development^2,3,4 A faster and more accurate method in defining disease cure using artificial intelligence and machine deep learning are also under development. All of these efforts in new chemical entity (NCE) development would eventually lead to the dosage forms, including injection and or oral formulation^5,6,7,8.

 

Furthermore, the oral formulation is preferred due to its convenience in both carrying and administration. However, the complexity in oral drugs ADME (absorption, distribution, metabolism, and excretion) processes makes drugs formulation difficult to design. Currently, there are various ADME simulation software available to help oral drugs formulation design. A number of reports provided valuable information in the area of new drugs development. Michael D. et. al. summarized the drugs ADME simulation over the past two decade⁹. Another study by Cheng L.et. al. also performed oral drug simulation strategy using Caco2 and human hepatocyte¹⁰. Additionally, Paulo P.et. al. reported better ADME simulation using PBPK model¹¹ for more than 100 drugs. All of these recent research have pointed out that ADME predicting software could lead to a better prediction of PK/PD^12,13. For oral drugs formulation development, traditionally, dissolution experiments were used to examine the performance of oral drug formulations¹⁴. However, a number of issues existed to make traditional dissolution study an less efficient system. For example, the buffer solutions used in dissolution tests as required per the U.S Food and Drug Administration (FDA) guidance, which often falls short in mimicking conditions in the gastrointestinal (GI) tract such as the various enzymes from bile and pancreatic secretions, resulting in poor correlation between in vitro and in vivo data¹⁵. This is especially true with active pharmaceutical ingredients (APIs) that have low aqueous solubility since solubility and permeability often influence one another^16,17. The issues that arise with traditional formulation development calls for a more accurate and cost-effective drug research strategy^18,19.

 

In vitro to in vivo correlation (IVIVC) techniques help reduce the length of drug development processes. A popular method involves the use of Caco-2 cells²⁰. However, because Caco-2 cells have to be incubated for up to twenty to thirty days before actual testing, it is not a time-effective way to improve IVIVC. The use of alive Caco-2 cells also makes this method costly. An alternative method that can be used to improve IVIVC for oral drugs prediction is Parallel Artificial Membrane Permeability Assay (PAMPA)²¹, which uses a chemically-based membrane instead of alive cells but has been proven to be able to accurately mimic the human small intestine^22,23,24. In this study, a PAMPA Dissolution system (Figure 1) is a newly designed instrument that combines dissolution and permeation in a way that closely simulates in vivo conditions. It measures the two necessary parameters—dissolution and permeation—for finding oral drug absorption via previously validated equation F (drug absorbed) = Cb*Pe* Area²⁵ and produces real-time graphs for dissolution and permeation. These graphs enable the calculation of Area Under Curve (AUC) and maximum plasma concentration (Cmax) values, which aid in predicting NDA formulation with respect to the RLD (Reference Listed Drugs) that share similar PK parameters to NCE. Unlike traditional dissolution tests, PAMPA Dissolution measures dissolution and permeation simultaneously similar to what it is like in vivo so that the solution does not undergo significant chemical changes before being transferred to permeation tests. Instead of traditional buffer solutions, PAMPA Dissolution uses biorelevant media, which contains the essential constituents in gastric, intestinal fluid and the needed pH values, further giving it more potential in predicting oral NDA formulation.

 

Figure 1. PAMPA Dissolution Apparatus

 

Materials and methods:

Materials：

Reagents, including NaH₂PO₄, NaOH, NaCl, HCl, phospholipids, DSMO, and n-dodecane were purchased from First Chemicals (Taiwan). FaSSIF/FeSSIF/FaSSGF powder (Biorelevant powder) was purchased from Biorelevant (London, UK).

 

Methods:

A newly designed PAMPA Dissolution system (YQ Biotech, Taiwan), as shown in Figure 1, was used to study oral formulations. Dissolution vessels are of USP 2 apparatus (TDTF, China). Fiber optic UV probes are inserted to record concentrations of drugs in real-time. On the PAMPA side of the apparatus, there are six vessels, and each of the vessel corresponding to one of the six on the dissolution side. Vessels on the PAMPA side have inner compartments that are filled with buffer at pH 6.5, with the addition of 1% DSMO and divided from the outer compartments by a 0.45μm hydrophobic membrane impregnated with 150μL of phospholipids. Fiber optic UV probes are also inserted in the inner compartments of the vessels on the PAMPA side.

 

Vessels on the dissolution side are each filled with 400 mL of pH 1.6 fasted state simulated gastric fluid (FaSSGF) made with Biorelevant powder, NaCl, and water. After 30 minutes, 100mL concentrated FaSSIF of pH 7.5 fluid consisting of Biorelevant powder, NaH₂PO₄, and NaOH is added in each vessel to turn the original fluid into fasted state simulated intestinal fluid (FaSSIF) of pH 6.5. Stirring on the dissolution side is set to 50-75rpm while the magnetic stirrer of PAMPA is set to 200rpm to minimize the unstirred water layer in order to mimic actual intestinal conditions as closely as possible.

 

Both PAMPA dissolution and FDA approved PK database were used in the development of a NDA drug formulation. Detail processes of obtaining RLD for the NDA formulation development were addressed in the following (Figure 2).

 

Figure 2. Alternative NDA scheme for oral drug development

 

RESULTS AND DISCUSSION:

An alternative method in oral NDA formulation development:

Traditionally, scale-up factors are introduced in predicting FIH (First in Human) dosage from pre-clinical study. In order to better predicting clinical oral dosage, the following procedures are proposed in Figure 2, which is based upon MRSD (Maximum Recommended Starting Dose), PAD (Pharmaceutical Active Dose), PAMPA Dissolution, and FDA approved PK database.

 

As shown in green and yellowish boxes of Figure 2, the proposed oral NDA formulation steps are:

1.     Based on preclinical and in vitro hepatocyte data, NCE drug’s permeability of Caco2 (Pe), clearance rate (Cl), and volume of distribution (Vd) can be derived to predict human PK.

2.     Proceed ADME simulation using PBPK modeling and or

3.     Compare predicted human PK data with FDA approved PK database to obtain comparable RLD.

4.     Perform PAMPA Dissolution of above RLD to obtain permeation for NDA formulation design.

5.     Develop oral NDA formulation with respect to both PAMPA Dissolution and stability study.

6.     Proceed with Phase I study.

 

Case Study 1: (assumed NCE e.g. Metolazone)

Figure 3 shows a typical example of an oral NDA formulation design (assumed NCE e.g. Metolazone) following proposed scheme in Figure 2. An oral NDA drug’s ADME profile can be simulated with the widely adopted PBPK model using predicted parameters of Pe, Cl, Vd, and the novel approach is to compare the predicted parameters of Pe (or F%), Cl, and Vd with FDA approved PK database to obtain the RLD of similar PK, as shown in the Table 1. This NDA drug (e.g. Metolazone) has a similar PK profile (yellowish area) to ezetimibe tablets (RLD).Therefore, both products’ plasma profile are shown in Figure 3 (lower left). The initial oral NDA formulation could be predicted and reformulated via PAMPA Dissolution with RLD in Figure 3 (lower right). The dose of NDA can then be further determined with its permeability and Cmax. Figure 3 indicates the scheme of an oral NDA drug development. This scheme could not only better predict oral formulation but also reduce clinical risk.

 

Figure 3. Typical Example of Proposed Oral NDA Formulation Development Scheme

 

Table 1. FDA approved PK data base for RLD souring

ID	Name	Year	Injectmg	Oral mg	Route	Vd (L)	Cl (L/h)	F (%)	t1/2 (h)	Cmax	Cmaxunit	Tmax (h)
500	Tocainide	1984		400-600 mg	Oral	117	8.5	100	15	31.5	Micromolar	1.25
562	Levocarnitine	1985	200 mg/ml	330 mg	Oral ,Inj	100.4	4	15	17.4	80	Micromolar	3.3
781	Mifepristone	2000		200-300 mg	Oral	102	1.6	69	18	4.6	Micromolar	1.5
902	Ezetimibe	2002		10mg	Oral	108	3.4	50	19	220	Nanomolar
997	Minocycline	1971	100 mg	50-100 mg	Oral, Inj	104	4.68	90	19	9.14	Micromolar	2
1149	Metolazone	1973		2.5-10 mg	Oral	104	5.4	65	14	273	Nanomolar	2

 

Table 2 Similar PK parameters, similar PK profile

Name	Year	Oral mg	Route	Vd (L)	Cl (L/h)	F (%)	t1/2 (h)	Cmax	Cmax unit	Tmax (h)
Methyldopa	1962	250-500	Oral	44.8	13.6	50	1.8	6	Micromolar	3
Raltegravir	2007	25-400	Oral	31	10	60	1.5	5	Micromolar	4

 

Case Study 2: (NCE vs. Raltegravir and Methyldopa)

Another example of “similar PK parameters, similar PK profile” is shown in Table 2, it clearly indicates both raltegravir and methyldopa having similar PK parameters (in yellow boxes). Therefore, any NDA drug’s PK is closer to PK listed in yellow boxes in Table 2 will have similar PK profile. In other words, the FDA approved PK database can be used to predict NDA’s PK profile by matching PK parameters. Further with NCE’s permeability and Cmax, the dose of a NDA can be determined. The PAMPA dissolution can therefore beemployed to optimize NDA drug formulation for similar PK drugs. The PK profiles for raltegravir and methyldopa is shown in Figure 4. As can be found from Figure 4, both drugs have similar DME (distribution, metabolism, and excretion) profile, which is the results of similar PK parameters, with the exception in drug absorption. The drug absorption rate is determined by dose, API particle sizes, adjuvants, and formulation processes, which could be tailored by using PAMPA Dissolution for NDA formulation development.

 

Figure4. Comparison of PK profiles of raltegravir and methyldopa Conclusion:

Oral drug formulation is a frequently adopted dosage form in new drugs development. With the tools of ADME simulation, PAMPA Dissolution, and FDA approved drugs PK database, it could potentially improve oral NDA drugs formulation design and its PK prediction. From the Case studies of 1 and 2, the rule of

thumb that “similar PK parameters, similar PK profile” could be concluded. Therefore, the proposed alternative oral formulation method in NDA development could not only shorten research time but also reduce clinical risk.

 

Acknowledgements：

This work is supported by YQ Biotech, Taiwan and Isuzu Optics, Taiwan.

 

CONFLICT OF INTEREST:

The authors have no conflicts of interest regarding this investigation.

 

References：

1.      Guidance for Industry: Exposure-Response Relationships — Study Design, Data Analysis, and Regulatory Applications. Center for Drug Evaluation and Research (CDER), USFDA, 2003.

2.      Bhagyashree L. Jejurikar, Sachin H. Rohane. Drug Designing in Discovery Studio. Asian J. Research Chem. 2021; 14(2):135-138.

3.      Uday M. Satpute, Sachin H. Rohane. Efficiency of AUTODOCK: Insilico study of Pharmaceutical Drug Molecules. Asian J. Research Chem. 2021; 14(1):92-96.

4.      Salsa Lina Agustin, Tri Widiandani, Suko Hardjono, Bambang Tri Purwanto. QSAR of Acyl pinostrobin derivatives as Anti-breast cancer against HER-2 receptor and their ADMET properties based on in silico Study. Research Journal of Pharmacy and Technology 2022; 15(10):4641-8.

5.      Yash Paul, Avinash S. Dhake, Milind Parle, Bhupinder Singh. Quantitative Structure Pharmacokinetic Relationship Studies for Drug Clearance of Quinolone Drugs. Research J. Pharm. and Tech. 2008; 1(2): 106-111.

6.      Shreyash D. Kadam, Denni Mammen, Deepak S. Kadam, Sudhakar G. Patil. In silico molecular docking against C-KIT Tyrosine Kinase and ADME studies of 3-Ethyl-2-(2,3,4-trifluoro-phenylimino)-thiazolidin-4-one derivatives. Asian Journal of Research in Chemistry. 2023; 16(1):55-4. doi: 10.52711/0974-4150.2023.00010.

7.      Radhika Chelamalla, Ajitha Makula. Molecular docking studies and ADMET Predictions of Pyrimidine Coumarin Scaffolds as Potential IDO Inhibitors. Asian J. Research Chem. 2017; 10(3): 331-340.

8.      Nikunj Patadiya, Vipul Vaghela. Design, in-silico ADME Study and molecular docking study of novel quinoline-4-on derivatives as Factor Xa Inhibitor as Potential anti-coagulating agents. Asian Journal of Pharmaceutical Research. 2022; 12(3): 207-1. doi: 10.52711/2231-5691.2022.00034.

9.      Michael Davies, Rhys D.O. Jones, Ken Grime, Rasmus Jansson-Löfmark, Adrian J. Fretland, Susanne Winiwarter, Paul Morgan, and Dermot F. McGinnity. Improving the Accuracy of Predicted Human Pharmacokinetics: Lessons Learned from the AstraZeneca Drug Pipeline Over Two Decades. Trends in Pharmacological Sciences.  2020; 41(6). doi:10.1016/j.tips.2020.03.004.

10.   Cheng Li, Tongtong Liu, Xiaoming Cui, Annette S. USS, and K.-C. Cheng. Development of In Vitro Pharmacokinetic Screens Using Caco-2, Human Hepatocyte, and Caco-2/Human Hepatocyte Hybrid Systems for the Prediction of Oral Bioavailability in Humans. Journal of Biomolecular Screening. 2007: 1084-1091. doi: 10.1177/1087057107308892.

11.   Paulo Paixaoa, Luis F. Gouveia, Jose A.G. Morais. Prediction of the human oral bioavailability by using in vitro and in silico drug related parameters in a physiologically based absorption model. Int. J. Pharmaceutics. 2012; 429: 84–98, doi:10.1016/j.ijpharm.2012.03.019.

12.   Ernesto Callegari, Manthena V.S. Varma and R. Scott Obach. Prediction of Metabolite-to-Parent Drug Exposure: Derivation and Application of a Mechanistic Static Model. Clin Transl Sci. 2020; 13: 520–528. doi:10.1111/cts.12734.

13.   Olavi Pelkonen, AriTolonen, Timo Korjamo, Miia Turpeinen, Hannu Raunio. From known knowns to known unknowns: predicting in vivo drug metabolites. Bioanalysis. 2009. doi: 10.4155/bio.09.32.

14.   Priyanka M. Salve, Shital V. Sonawane, Mayuri B. Patil, Rajendra K. Surawase. Dissolution and Dissolution Test Apparatus: A Review. Asian Journal of Research in Pharmaceutical Sciences. 2021; 11(3): 229-6.

15.   Manju Nagpal, Pankaj Rakha, Surinder Goyal, Gitika Dhingra, Sunil Gupta. Comparison of Biorelevant and Compendial Dissolution Media and Prediction of In-vivo Plasma Profile of BCS Class II Drug. Research J. Pharma. Dosage Forms and Tech. 2010; 2(1): 37-40.

16.   Sanjay Kshirsagar, Manisha Choudhari, Reshmi Sathyan, Shruti Dhore. Solubility Enhancement by Various Techniques based on Pharmaceutical and Medicinal Chemistry Approach: An Overview. Asian J. Pharm. Tech. 2019;  9(2): 141-146.

17.   Manohar D. Kengar, Rohit S. Howal, Dattatray B. Aundhakar, Amit V. Nikam, Priyajit S. Hasabe. Physico-chemical Properties of Solid Drugs: A Review. Asian J. Pharm. Tech. 2019; 9 (1): 53-59.

18.   Jantratid E, Janssen N, Reppas C, Dressman JB. Dissolution media simulating conditions in the proximal human gastrointestinal tract: an update. Pharm Res. 2008 Jul; 25(7): 1663-76. doi: 10.1007/s11095-008-9569-4. Epub 2008 Apr 11. PMID: 18404251.

19.   Mann J, Dressman J, Rosenblatt K, Ashworth L, Muenster U, Frank K, Hutchins P, Williams J, Klumpp L, Wielockx K, Berben P, Augustijns P, Holm R, Hofmann M, Patel S, Beato S, Ojala K, Tomaszewska I, Bruel JL, Butler J. Validation of Dissolution Testing with Biorelevant Media: An OrBiTo Study. Mol Pharm. 2017 Dec 4; 14(12): 4192-4201. doi: 10.1021/acs.molpharmaceut.7b00198. Epub 2017 Aug 23. PMID: 28737403.

20.   Ina Hubatsch, Eva G E Ragnarsson and Per Artursson. Determination of drug permeability and prediction of drug absorption in Caco-2 monolayers. Nature Protocols. 2007; 2(9): 2113. doi:10.1038/nprot.2007.303.

21.   Sugano K, Hamada H, Machida M, Ushio H, Saitoh K, Terada K. Optimized conditions of bio-mimetic artificial membrane permeation assay. Int J Pharm. 2001 Oct 9; 228(1-2): 181-8. doi: 10.1016/s0378-5173(01)00845-6. PMID: 11576780.

22.   Nielsen PE, Avdeef A. PAMPA--a drug absorption in vitro model 8. Apparent filter porosity and the unstirred water layer. Eur J Pharm Sci. 2004 May; 22(1): 33-41. doi: 10.1016/j.ejps.2004.02.003. PMID: 15113581.

23.   Avdeef A, Artursson P, Neuhoff S, Lazorova L, Gråsjö J, Tavelin S. Caco-2 permeability of weakly basic drugs predicted with the double-sink PAMPA pKa(flux) method. Eur J Pharm Sci. 2005 Mar; 24(4): 333-49. doi: 10.1016/j.ejps.2004.11.011. Epub 2005 Jan 20. PMID: 15734300.

24.   Bermejo M, Avdeef A, Ruiz A, Nalda R, Ruell JA, Tsinman O, González I, Fernández C, Sánchez G, Garrigues TM, Merino V. PAMPA--a drug absorption in vitro model 7. Comparing rat in situ, Caco-2, and PAMPA permeability of fluoroquinolones. Eur J Pharm Sci. 2004 Mar; 21(4): 429-41. doi: 10.1016/j.ejps.2003.10.009. PMID: 14998573.

25.   Sinko, Patrick and Leesman, Glen and Amidon, Gordon. Predicting Fraction Dose Absorbed in Humans Using a Macroscopic Mass Balance Approach. Pharmaceutical Research. 1991; 8. Doi：10.1023/A:1015892621261.

 

 

 

 

Received on 05.08.2023         Modified on 26.02.2024

Accepted on 31.05.2024   ©AandV Publications All Right Reserved