Roberto Gomeni, Ph.D.PharmacoMetrica

Adjunct Professor, Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, Chappel Hill, NC

roberto.gomeni@pharmacometrica.comwww.pharmacometrica.com

3rd PQRI/FDA Conference on Advancing Product QualityMarch 22-24, 2017. Rockville, Maryland.

22

• Modeling framework

• Modeling delayed in-vivo absorption processes

• Convolution-based modeling approach

• Modeling LAI products

• IVIVC for LAI products

Long Acting Injectable (LAI) formulation: a formulation able to insure a prolonged clinical effect by delivering a pharmacological agent to the systemic circulation with a slow release over an extended period of time or with the ability to insure a continued absorption of small amounts of the dosage of the active ingredient over an extended period of time.

33

Modelling strategy

In-Vitro Experiments

In-Vivo release process

In-Vivo disposition

Exposure-response

relationship

Relationship between

response and Clinical Benefit

IVIVC

Pharmacokinetics

Pharmacodynamics

Pharmacotherapy

Connect the In-Vitro dissolution with the In-Vivo drug release

Describe the drug PK as a function of the In-Vivo drug release and the In-Vivo disposition/elimination processes

Describe the PD response as a function of the PK

Optimize the drug therapy

44

• The plasma concentration-time profiles following

LAI administration of drugs are often irregular and

cannot be interpreted easily with conventional

models based on first-order absorption kinetics and

lag time

• The first-order absorption kinetics is in the majority

of the circumstances an oversimplified approach,

which does not account for the complexity of the In-

Vivo absorption process of a LAI formulation

55

Alternative models are considered:

Zero and first order process

: Estimation of lag time

: Delay due to

movement of drug from the depot to the systemic

circulation through a chain of transit compartments

: Delay due to a

complex in-vivo release process

: Extension of the time-varying

method to match the in-vitro dissolution

66

T

+T

𝑑𝐴

𝑑𝑡= 𝑟𝑎𝑡𝑒 − 𝑘𝑒𝑙 ∙ 𝐴

𝑑𝐴2

𝑑𝑡= 𝑘𝑎 ∙ 𝐴1 − 𝑘𝑒𝑙 ∙ 𝐴2

𝑑𝐴1

𝑑𝑡= −𝑘𝑎 ∙ 𝐴1

𝑑𝐴2

𝑑𝑡= 𝑘𝑎 ∙ 𝐴1 + 𝑟𝑎𝑡𝑒 − 𝑘𝑒𝑙 ∙ 𝐴2

𝑑𝐴1

𝑑𝑡= −𝑘𝑎 ∙ 𝐴1

𝑘𝑒𝑙 =𝐶𝐿

𝑉𝑟𝑎𝑡𝑒 =

0 𝑖𝑓 𝑡𝑖𝑚𝑒 > 𝑇𝑟𝑎𝑡𝑒 𝑜𝑓 𝑟𝑒𝑙𝑒𝑎𝑠𝑒 𝑖𝑓 𝑡𝑖𝑚𝑒 ≤ 𝑇

77

88

The drug absorption process from the depot site is

defined by a dual first order process:

𝑑𝐴1

𝑑𝑡= −𝑘𝑎1 ∙ 𝐴1

𝑑𝐴2

𝑑𝑡= −𝑘𝑎2 ∙ 𝐴2

𝑑𝐴3

𝑑𝑡= 𝑘𝑎1 ∙ 𝐴1 + 𝑘𝑎2 ∙ 𝐴2 − 𝑘𝑒𝑙 ∙ 𝐴3

𝐴1 𝑡𝑖𝑚𝑒 = 0 = 𝑓𝑓 ∙ 𝐷𝑜𝑠𝑒𝐴2 𝑡𝑖𝑚𝑒 = 0 = (1 − 𝑓𝑓) ∙ 𝐷𝑜𝑠𝑒

ka1

ff 1-ff

kel

A1 A2

ka2lag1 lag2

A3

99

Radojka M. Savic, Daniël M. Jonker, Thomas Kerbusch & Mats O Karlsson Evaluation of a transit compartment model versus a lag time model for describing drug absorption delay PAGE 13 (2004) Abstr 513 [www.page-meeting.org/?abstract=513]

1010

ka1kk

kk

kk

3

4

5

6

7

ff 1-ff

kk

kel

𝑑𝐴1

𝑑𝑡= −𝑘𝑎1 ∙ 𝐴1

𝑑𝐴2

𝑑𝑡= −𝑘𝑘 ∙ 𝐴2

𝑑𝐴3

𝑑𝑡= 𝑘𝑘 ∙ (𝐴2-𝐴3)

𝑑𝐴4

𝑑𝑡= 𝑘𝑘 ∙ (𝐴3-𝐴4)

𝑑𝐴5

𝑑𝑡= 𝑘𝑘 ∙ (𝐴4-𝐴5)

𝑑𝐴6

𝑑𝑡= 𝑘𝑘 ∙ (𝐴5-𝐴6)

𝑑𝐴7

𝑑𝑡= 𝑘𝑎1 ∙ 𝐴1 + 𝑘𝑘 ∙ 𝐴6 − 𝑘𝑒𝑙 ∙ 𝐴7

𝐴1 𝑡𝑖𝑚𝑒 = 0 = 𝑓𝑓 ∙ 𝐷𝑜𝑠𝑒

𝐴2 𝑡𝑖𝑚𝑒 = 0 = (1 − 𝑓𝑓) ∙ 𝐷𝑜𝑠𝑒

A1 A2

1111

Radojka M. Savic, Daniël M. Jonker, Thomas Kerbusch & Mats O Karlsson Evaluation of a transit compartment model versus a lag time model for describing drug absorption delay PAGE 13 (2004) Abstr 513 [www.page-meeting.org/?abstract=513]

1212

• To describe the complex patterns of plasma-concentration

time profiles it is necessary to use mechanistic models

that incorporate the different physiological factors

involved in the oral absorption process

• Appropriate models can be developed using a time-

dependent absorption rate coefficient, ka(t), wherein the

time dependency varies to account for the dynamic

processes such as changes in fluid absorption or

secretion, in absorption surface area, in motility with time,

and in the gastrointestinal tract

Higaki K, Yamashita S, Amidon GL. Time-dependent oral absorption models.J Pharmacokinet Biopharm 2001 28(2):109-28

1313Higaki K, Yamashita S, Amidon GL. Time-dependent oral absorption models.J Pharmacokinet Biopharm 2001 28(2):109-28

𝑡𝑣𝑘𝑎 = 𝑘𝑎 ∗ (1 − 𝑒(𝑡ℎ

)𝑏 ) 𝑑𝐴1

𝑑𝑡= −𝑡𝑣𝑘𝑎 ∙ 𝐴1

𝑑𝐴2

𝑑𝑡= 𝑡𝑣𝑘𝑎 ∙ 𝐴1 − 𝑘𝑒𝑙 ∙ 𝐴2

1414

• The process of dissolution is of fundamental importance for the

bioavailability and, hence, therapeutic efficacy of various treatments

• Different physical phenomena are involved in the process of drug dissolution

in an aqueous body fluid (i.e. wetting of the particle's surface, breakdown of

solid state bonds, solvation, diffusion through the liquid unstirred boundary

layer surrounding the particle as well as convection in the surrounding bulk

fluid,…)

• Appropriate mathematical equations can be used to quantify these mass

transport steps, and more or less complex theories can be developed to

describe the resulting drug dissolution kinetics

Develop an integrated in-vivo model assuming that the absorption

process can be described by the same model used to characterize

the in-vitro dissolution data

1515

1616

Wang Y, Lee L, Somma R, Thompson G, Bakhtiar R, Lee J, Rekhi GS, Lau H, Sedek G, Hossain M. In vitro dissolution and in vivo oral absorption of methylphenidate from a bimodal release formulation in healthy volunteers. Biopharm Drug Dispos. 2004 Mar;25(2):91-8.

1717

In case of a simple disposition process the model equation describing Cp(t) can be written as:

r(t) = time-varying fraction of the dose released f(t) = the first derivative of r(t). This can be computed analytically or can

be approximated using the finite difference approach

t

p dτiv(ττ)f(tf(t)*iv(t)(t)C0

)f(t) = In-Vivo input rateiv(t) = Unitary Impulse Response

𝑓 𝑡 =𝑑𝑟

𝑑𝑡*iv(t)

dt

tdr(t)C p

)(

1818

Assuming a one compartment linear model with a Weibull In-Vivo drug release r(t). The convolution-based model using thefinite difference approximation can be implemented as:

𝑟 𝑡 = 𝑒−

𝑡𝑖𝑚𝑒𝑡𝑑

𝑠𝑠

DELT=0.001. . . . . . . $DES

TT1=T-DELTTT2=T+DELTIF(TT1.LE.0 )TT1=0IF(TT2.LE.0) TT2=0ABS1=EXP(-(TT1/TD)**SSABS2=EXP(-(TT2/TD)**SSFt=(ABS1-ABS2)/(TT2-TT1)DADT(1)=-A(1)*FtDADT(2)=A(1)*Ft-KEL*A(2)

𝑓 𝑡 =𝑑𝑟

𝑑𝑡=

𝑟 𝑡 − ∆ − 𝑟(𝑡 + ∆)

2 ∙ ∆

𝑓 𝑡 =𝑑𝑟

𝑑𝑡

1919

The drug absorption rate from the depot is defined by

a dual Weibull process:

𝑤1(𝑡) = 𝑓 ∙𝑠𝑠

𝑡𝑑∙

𝑡𝑖𝑚𝑒

𝑡𝑑

(𝑠𝑠−1)∙ 𝑒

−𝑡𝑖𝑚𝑒

𝑡𝑑

𝑠𝑠

𝑤2(𝑡) = (1 − 𝑓) ∙𝑠𝑠1

𝑡𝑑1∙

𝑡𝑖𝑚𝑒

𝑡𝑑1

(𝑠𝑠1−1)∙ 𝑒

−𝑡𝑖𝑚𝑒

𝑡𝑑1

𝑠𝑠1

f(t)= 𝑤1(𝑡)+ 𝑤2(𝑡)

Absorption rate (f(t) = dPA/dt):

𝑎𝑤1(𝑡) = 𝑓 ∙ 𝑒−

𝑡𝑖𝑚𝑒𝑡𝑑

𝑠𝑠

𝑎𝑤2(𝑡) = (1 − 𝑓) ∙ 𝑒−

𝑡𝑖𝑚𝑒𝑡𝑑1

𝑠𝑠1

r(t)= 𝑎𝑤1(𝑡)+ a𝑤2(𝑡)

% absorbed (PA):

f = fraction of the dose released in the 1st process

td = time to absorb 63.2% of the dose released in the 1st

processtd1= time to absorb 63.2% of the

dose released in the 2nd

processss= sigmoidicy factor for the 1st

processss1= sigmoidicity factor for the 2nd

process

*iv(t)dt

tdr(t)C p

)(

2020

75 mg eq. 150 mg eq. 175 mg eq. 300 mg eq. 350 mg eq. 450 mg eq. 525 mg eq.

0

100

200

300

400

0 24 48 72 0 24 48 72 0 24 48 72 0 24 48 72 0 24 48 72 0 24 48 72 0 24 48 72Time (weeks)

75 mg eq. 150 mg eq. 175 mg eq. 300 mg eq. 350 mg eq. 450 mg eq. 525 mg eq.

1

10

100

0 24 48 72 0 24 48 72 0 24 48 72 0 24 48 72 0 24 48 72 0 24 48 72 0 24 48 72Time (weeks)

Co

nce

ntr

atio

n (

ng

/mL

)

75 mg eq. 150 mg eq. 175 mg eq. 300 mg eq. 350 mg eq. 450 mg eq. 525 mg eq.

0

100

200

300

400

0 24 48 72 0 24 48 72 0 24 48 72 0 24 48 72 0 24 48 72 0 24 48 72 0 24 48 72Time (weeks)

75 mg eq. 150 mg eq. 175 mg eq. 300 mg eq. 350 mg eq. 450 mg eq. 525 mg eq.

1

10

100

0 24 48 72 0 24 48 72 0 24 48 72 0 24 48 72 0 24 48 72 0 24 48 72 0 24 48 72Time (weeks)

Co

nce

ntr

atio

n (

ng

/mL

)

Magnusson MO et al.. Population Pharmacokinetics of a Novel Once-Every 3 Months Intramuscular Formulation of Paliperidone Palmitate in Patients with Schizophrenia. ClinPharmacokinet. 2016 Oct 14.

Invega Trinza: a 3-month paliperidone palmitate formulation

Heres S et al. Pharmacokinetics of olanzapine long-acting injection: the clinical perspective. Int ClinPsychopharmacol. 2014 Nov;29(6):299-312.

Olanzapine pamoate[olanzapine long-acting

injection (OLAI)]

2121

t1=time-delt t2=time+delt

𝑎𝑏𝑠1 = 𝑓 ∙ 𝑒−

𝑡1𝑡𝑑

𝑠𝑠

+ (1 − 𝑓) ∙ 𝑒−

𝑡1𝑡𝑑1

𝑠𝑠1

𝑎𝑏𝑠2 = 𝑓 ∙ 𝑒−

𝑡2𝑡𝑑

𝑠𝑠

+ (1 − 𝑓) ∙ 𝑒−

𝑡2𝑡𝑑1

𝑠𝑠1

𝑘𝑎𝑏 =(𝑎𝑏𝑠1 + 𝑎𝑏𝑠2)

(𝑡2 − 𝑡1)

𝑑𝐴1

𝑑𝑡= −𝑘𝑎𝑏 ∙ 𝐴1

𝑑𝐴2

𝑑𝑡= 𝑘𝑎𝑏 ∙ 𝐴1 − (𝑘𝑒𝑙 + 𝑘12) ∙ 𝐴2 + 𝑘21 ∙ 𝐴3

𝑑𝐴3

𝑑𝑡= 𝑘12 ∙ 𝐴2 − 𝑘21 ∙ 𝐴3

𝐴1(𝑡𝑖𝑚𝑒 = 0) = 𝐷𝑜𝑠𝑒 [Depot]

Release process Time-varying kab function

2222

• The in-vivo input function f(t) can be characterized using the same model used for describing the in-vitro dissolution data (r(t))

• This modeling strategy, usually referred as convolution-based differential equation, enables to establish an in-vitro-in-vivo correlation ( ) when the same model properly describe either the in-vitro dissolution or the in-vitro release processes*

* EMEA Guideline on the pharmacokinetic and clinical evaluation of modified release dosage forms (2014) http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2014/11/WC500177884.pdf

r(t)𝑓 𝑡 =

𝑑𝑟

𝑑𝑡

2323

Model based approach where the entire in vivo time

course should be predicted by the in vitro data

Point to point correlation between the fraction of drug

absorbed (in vivo input rate) and the fraction of drug

dissolved

“.. In a linear correlation, the in vitro dissolution and in

vivo input curves may be directly superimposable or may

be superimposable by the use of a scaling factor. ..”

Correlation should be established based on average data

Use deconvolution to estimate the in vivo input rate curve

2424

Convolution – based model

Absorption

Absorption function = r(t)

The plasma drug-concentration-vs.-time curve can be viewed as the

resultant of the combined processes relating drug absorption,

distribution and elimination

The output function Cp(t) can be estimated as the convolution of a

input function r(t), with a disposition function iv(t)

Disposition

Disposition function = iv(t)

+

Plasma curve

Output function = Cp(t)

=

*iv(t)dt

tdr(t)C p

)(

2525

Convolution Analysis

Time (day)

+

0

2

4

6

8

10

0 2 4 6 8 10

Con

c. (

ng/m

l)

Time (day)

0

1

2

3

4

5

0 5 10 15 20 25

Co

nc.

(n

g/m

l)

0

20

40

60

80

100

0 2 4 6 8 10

Time (day)

% D

isso

lved

*iv(t)dt

tdr(t)C p

)(

2626

In Level A correlation, In-Vitro dissolution and In-Vivo

input may be directly superimposable or may be

made superimposable by the use of a scaling factor

However, this not always occurs and differences

between In-Vitro and In-Vivo profiles are often

observed

Time-scaling can be used to account for a possible

difference between In-Vitro and In-Vivo release

profiles and to determine the time correction factor for

the in In-Vitro data enabling the achievement of an

acceptable superposition with the In-Vivo

measurements

2727

• A linear regression analysis was used to assess the

Level A IVIVC by evaluating a point to point correlation

between the fraction of drug absorbed (rvivo = the r(t)

function values estimated in-vivo) and the fraction of

drug dissolved (rvitro = the r(t) function values estimated

in-vitro).

• A linear time scaling model was applied in the correlation

analysis to account for the different time scale between

the in-vivo and the in-vitro data:

In case of identity between rvivo and rvitro a1 = 0, a2 = 1, b1 = 0, and b2 = 1. This

relationship includes a linear component (intercept of a1 and slope of a2), and a

nonlinear component describing the time-shifting (b1) and time-scaling (b2)

2828

The IVIVC analysis was conducted using a 4-step

approach:

1. Fit the r(t) model [in-vitro dissolution] to the dissolution data for

each formulation

2. Fit the in-vivo disposition model (iv(t)) to the IV PK data

3. Fit the convolution-based model to the in-vivo PK data

(Cp(t)) after LAI administration:

by fixing :

• the r(t) parameters to the values estimated in step 1

• the iv(t) parameters to the values estimated in step 2

by estimating the time scaling parameters common for all the

formulations: a1, a2, b1 and b2

4. Evaluate the predictability of the model by comparing the values of

Cmax and AUC predicted in step 3 with the observed values

2929

• The level A IVIVC relationship was evaluated using data from a

relative bioavailability of 3 formulations (slow, target (commercially

available), and fast release rates after administration of a single 12

mg dose) with different in vitro release profiles in healthy subjects and

an in-vitro study to evaluate the drug release using a USP release

rate apparatus type VII

• The mean PK data and the mean dissolution data used for model

development were extracted from the FDA approval package for

paliperidone*

* FDA Center for drug evaluation and research (2009). Approval package for Application number: nda 21-999/S-004 http://www.accessdata.fda.gov/drugsatfda_docs/nda/2009/021999Orig1s004.pdf

3030

In-Vivo: Two-compartment model with first order absorption rate

In-Vitro: Double Weibull model

3131

Vd=V/Bioav t1=time-delt t2=time+delt tim=(b1+b2*t1)

𝐴𝐵1 = 𝑓 ∙ 𝑒−

𝑡𝑖𝑚𝑡𝑑

𝑠𝑠

+ (1 − 𝑓) ∙ 𝑒

− 𝑡𝑖𝑚𝑡𝑑1

𝑠𝑠1

abs1=a1+a2∙AB1 tim=(b1+b2*t2)

𝐴𝐵2 = 𝑓 ∙ 𝑒−

𝑡𝑖𝑚𝑡𝑑

𝑠𝑠

+ (1 − 𝑓) ∙ 𝑒

− 𝑡𝑖𝑚𝑡𝑑1

𝑠𝑠1

abs2=a1+a2∙AB2

𝑘𝑎𝑏 =(𝑎𝑏𝑠1 + 𝑎𝑏𝑠2)

(𝑡2 − 𝑡1)

𝑑𝐴1

𝑑𝑡= −𝑘𝑎𝑏 ∙ 𝐴1

𝑑𝐴2

𝑑𝑡= 𝑘𝑎𝑏 ∙ 𝐴1 − (𝑘𝑒𝑙 + 𝑘12) ∙ 𝐴2 + 𝑘21 ∙ 𝐴3

𝑑𝐴3

𝑑𝑡= 𝑘12 ∙ 𝐴2 − 𝑘21 ∙ 𝐴3

𝐶𝑝 = 𝐴2/𝑉𝑑

A1(time = 0) = Dose [Depot]

If (formulation=1) then td=44.3 ss=0.5

td1=22.7 ss1=7.9 f=0.2 endif if(formulation=2) then … … Endif

• Parameters estimated by fitting the in-vitro dissolution data

• Parameters estimated by fitting the IV or the IR data

• Parameters to be estimated for establishing IVIVC

3232

R. Gomeni, F. Bressolle, M. Fava. Response surface analysis and non-linear optimization algorithm for maximization of clinical drug performance: Application to extended release and long-acting-injectable paliperidone. J Clin Pharmacol. 2016 Oct; 56(10):1296-306.

3333

Predictability criteria:

• PE <15% for each formulation

• PE <10% for mean values

n

1

100 valueObs.

valuePred. - valueObs.

n

1PEPrediction Error(PE):

The highest mean prediction errors is less than 15% for the individual formulations and the highest mean absolute prediction error for the 3 formulations is 3.43% for Cmax. This value is less than the maximum allowable prediction errors 10% for mean absolute prediction error

34

• RBP-7000 is a once-monthly formulation of risperidone subcutaneously injected using the ATRIGEL® Delivery System.

• The ATRIGEL® Delivery System consists of biodegradable polymers and carriers, in which are formulated the active pharmaceutical agent

• When the liquid polymer system is placed in the body, it solidifies upon contact with aqueous body fluids to form a solid implant. If a drug is incorporated into the polymer solution, it becomes entrapped within the polymer matrix as it solidifies, and is slowly released as the polymer biodegrades

A dual absorption mechanism :1)An immediate release

associated with the first observed peak

2)A delayed delivery associated with the ATRIGEL® Delivery System

35

--------- SC injection site ---------- 𝑑𝐴𝑏𝑠

𝑑𝑡= −(𝑘𝑎1 + 𝑘𝑎2) · 𝐴𝑏𝑠

--------- Transit compartment model ---------- 𝑑𝑇𝑟1

𝑑𝑡= 𝑘𝑎2 · 𝐴𝑏𝑠 − 𝑘𝑡𝑟 · 𝑇𝑟1

𝑑𝑇𝑟2

𝑑𝑡= (𝑇𝑟1 − 𝑇𝑟2) · 𝑘𝑡𝑟

𝑑𝑇𝑟3

𝑑𝑡= (𝑇𝑟2 − 𝑇𝑟3) · 𝑘𝑡𝑟

𝑑𝑇𝑟4

𝑑𝑡= (𝑇𝑟3 − 𝑇𝑟4) · 𝑘𝑡𝑟

𝑑𝑇𝑟5

𝑑𝑡= (𝑇𝑟4 − 𝑇𝑟5) · 𝑘𝑡𝑟

--------- Risperidone model ---------- 𝑑𝑟𝑖𝑠𝑝𝐶

𝑑𝑡= 𝑘𝑎1 · 𝐴𝑏𝑠 + 𝑘𝑡𝑟 · 𝑇𝑟5 + 𝑘𝑟𝑝𝑟 · 𝑟𝑖𝑠𝑝𝑃 − (𝑘𝑟𝑒𝑙 + 𝑘𝑟𝑟𝑝 + 𝑘𝑟9) · 𝑟𝑖𝑠𝑝𝐶

𝑑𝑟𝑖𝑠𝑝𝑃

𝑑𝑡= 𝑘𝑟𝑟𝑝 · 𝑟𝑖𝑠𝑝𝐶 − 𝑘𝑟𝑝𝑟 · 𝑟𝑖𝑠𝑝𝑃

--------- 9-hydroxyrisperidone model ---------- 𝑑9𝑂𝐻

𝑑𝑡= 𝑘𝑟9 · 𝑟𝑖𝑠𝑝𝐶 − 𝑘9𝑒𝑙 · 9𝑂𝐻

• 1st order absorption process associated with the rapid absorption and the first observed plasma concentration peak

• Delayed delivery process described by a transit compartment model that mimicked the ATRIGEL® Delivery System

• Two-compartment model for the disposition and elimination of risperidone

• 1st order conversion process from risperidone to 9-OH

36

The adequacy of the final model, including the effects of statistically significant covariates was evaluated using the visual predictive check (VPC) method.

Five-hundred replicates of the original dataset were simulated based on the final model, and a 90% prediction interval was computed based on the simulated datasets.

• BMI was identified as a significant covariate affecting the magnitude of the two PK peaks for both risperidone and for 9-OH : Patients with smaller BMI showed a higher initial peak

• The volume of distribution of risperidone and of 9-OH decreased with the increase of the dose of RBP-7000

R. Gomeni, C. Heidbreder, P.J. Fudala, A.F. Nasser. A model-based approach to characterize the population pharmacokinetics and the relationship between the pharmacokinetic and safety profiles of RBP-7000, a new, long-acting, sustained-release formulation of risperidone. J Clin Pharmacol. 2013; 53(10):1010-9

37

The objective of this study was to compare the in vitro behavior of four long-acting subcutaneous risperidone formulations with in vivo performance, with the intent of establishing an IVIVC. Two copolymers of PLGA (50:50 and 75:25) were used to prepare four microsphere formulations of risperidone, an atypical antipsychotic.

D’Souza S., Faraj J.A., Giovagnoli S., DeLuca P.P..Development of Risperidone PLGA Microspheres. Journal of Drug Delivery. 2014, 620464, 11

Formulation D: Scanning electron microscopy of Risperidone PLGA

microspheres

Formulation D: In vivo PK time course

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Shen J, Choi S, Qu W, Wang Y, Burgess DJ. In vitro-in vivo correlation of parenteral risperidone polymeric microspheres. J Control Release. 2015 Nov 28;218:2-12.

In vitro release of the risperidone PLGA microspheres was investigated using 4 release testing methods. In vivo PK profiles of the risperidone microsphere was evaluated using:• IV dose of 0.2 mg/kg• intramuscular administration of the

prepared risperidone PLGA microspheres at a dose of 1.92 mg/kg

Burst release

3939

t1=time-delt t2=time+delt

𝑎𝑏𝑠1 = 𝑓 ∙ 𝑒−

𝑡1𝑡𝑑

𝑠𝑠

+ (1 − 𝑓) ∙ 𝑒−

𝑡1𝑡𝑑1

𝑠𝑠1

𝑎𝑏𝑠2 = 𝑓 ∙ 𝑒−

𝑡2𝑡𝑑

𝑠𝑠

+ (1 − 𝑓) ∙ 𝑒−

𝑡2𝑡𝑑1

𝑠𝑠1

𝑘𝑎𝑏 =(𝑎𝑏𝑠1 + 𝑎𝑏𝑠2)

(𝑡2 − 𝑡1)

𝑑𝐴1

𝑑𝑡= −𝑘𝑎𝑏 ∙ 𝐴1

𝑑𝐴2

𝑑𝑡= 𝑘𝑎𝑏 ∙ 𝐴1 − (𝑘𝑒𝑙 + 𝑘12) ∙ 𝐴2 + 𝑘21 ∙ 𝐴3

𝑑𝐴3

𝑑𝑡= 𝑘12 ∙ 𝐴2 − 𝑘21 ∙ 𝐴3

𝐴1(𝑡𝑖𝑚𝑒 = 0) = 𝑓𝑓 ∙ 𝐷𝑜𝑠𝑒 [Depot]𝐴2 𝑡𝑖𝑚𝑒 = 0 = (1 − 𝑓𝑓) ∙ 𝐷𝑜𝑠𝑒 [Plasma comp]

4040

In-Vivo: Two-compartment modelIn-Vitro: Double Weibull model

4141

4242

Predictability criteria:

• PE <15% for each formulation

• PE <10% for mean values

n

1

100 valueObs.

valuePred. - valueObs.

n

1PEPrediction Error(PE):

The highest mean prediction errors is less than 15% for the individual formulations and the highest mean absolute prediction error for the 3 formulations is 6.52% for Cmax. This value is less than the maximum allowable prediction errors 10% for mean absolute prediction error

4343

The Convolution-based modeling approach presented

provided:

• A tool for developping models appropriate for

characterizing the complex absorption proces and

the PK time course of LAI formlations

• A framework for developping IVIVC for LAI

formulatons