Prof. Antonio Pellicer Instituto Valenciano de Infertilidad (IVI)

University of Valencia apellicer@ivi.es

www.ivi.es

Improving outcomes in ART : Time-lapse technology for monitoring COS

and blastocyst culture

DISCLOSURE

- Invitation by an unrestricted Educational Grant from

COMTECMED to ASRM - IVI is a minor shareholder in Unisense Fertilitech A/S.

- IVI is a minor shareholder in Auxogyn Co. - This work has not received any financial support from any

commercial entity and the instrumentation, disposables and utensils belong to IVI.

EMBRYONIC IMPLANTATION

MOLECULAR

DIALOGUE

-

Health embryo at blastocyst stage

Adequate Endometrial Receptivity

To select the best embryo/s

HUMAN EMBRYONIC IMPLANTATION

Improvement of ART outcomes

Personalized Embryo Transfer (pET)

Endometrial receptivity assay (ERA)

Other non-invasive methods

Identification/Modification of receptive endometrium

Window of Implantation

Identification of the viable embryo

Invasive methods: CCS (D3 or D5)

Non-invasive methods:

Morphology

Time-lapse Proteomics

Metabolomics

Improvement of ART outcomes

Personalized Embryo Transfer (pET)

Identification of the viable embryo

Repeated implantation failure (RIF)

Aged patients

Reduced ovarian reserve

Endometriosis

Severe male factor

Recurrent miscarriage

Improvement of ART outcomes

Personalized Embryo Transfer (pET)

Identification of the viable embryo

Time-lapse

Invasive methods: CCS (D3 or D5)

….in ALL ART CYCLES?

✔

✘

Time-Lapse Technology

Time-Lapse Imaging - Blastomere Activity

PÁG.8

Time-Lapse Development cc2= t3-t2

t5

CC2

Time post insemination, hours

0 5 10 15 20 25 30

coun

t

0

500

1000

1500

2000

2500

Regular divisionsViable 8 cellViable blastocystImplanted

t5

Time post insemination, hours

30 40 50 60 70 80

coun

t

0

200

400

600

800

1000

1200Regular divisionsViable 8 cellViable blastocystImplanted

PÁG.9

Best correlation

with implantation

success

Predictive ability of embryo implantation

PÁG.10

715, 14%

4510, 86%

Incidence rate of direct division 1-3 in all embryos deviding to 3 cells

Direct division 1-3cells

No direct division1-3 cells

0

10

20

30

DC 1-3 Not DC1-3

2,9 %

28,7%

Impl

anta

tion

Rat

e

*P<0.0001 *

Rubio et al. Fertil Steril 2012; 98(6)

PÁG.11

Morphology

included

ok

Grade A Grade B Grade C Grade D Grade E Discarded

non viable

excluded

yes no

yes no no yes

PÁG.A+

PÁG.11A B+ B C+ C D+ D

CC2 5- 12h CC2 5-12h CC2 5-12h CC2 5-12h

yes no yes no yes no yes no

included

Exclusion Criteria

Direct Cleavage Uneven Blastomere

T5

48-56h

T3

35-40h

T3

35-40h

PÁG.12

Time-Lapse: Initial findings

Embryo morphology correlates with embryo classification by time-lapse

Embryo quality and implantation correlate with embryo classification by time-lapse

In a retrospective study, time-lapse (n=1372 cycles) as compared to conventional incubators (n=5872 cycles):

reduced significantly (2.8% vs 5.2%) cycle cancellation rates

Increased significantly (59.1 vs 50%) ongoing pregnancy rates

Meseguer et al. Fertil Steril 2012; 98:1481-9

PÁG.13

Randomized Controlled Trial

Rubio I. et al. Fertil Steril 2014; 102: 1287-94

PÁG.14

Inclusion Criteria ICSI

MII ≥6

Age 20-38

Previous Cycles ≤2

BMI 18-25

Basal FSH <12

AMH >7 pmol/L

Exclusion Uterine Pathologies

Hydrosalpinx

Recurrent Miscarriage

Endometriosis

< 1 mill progressive sperm (A+B)

PÁG.15

Not meeting inclusion criteria (n=52) • Patient request TMS, n=30 • IVF as fertilization procedure, n=14. • Testicular sperm or cripto, n=5. • Already randomized, n=1. • Advanced maternal age, n=1. • Low respond, n=1.

Not meeting inclusion criteria (n=22) • No embryoslides available, n=8 • IVF as fertilization procedure, n=5. • Testicular Sperm or Cripto, n=5. • Already randomized, n=1. • Low respond, n=3.

SI group

Allocated to intervention(n=412) Received allocated to intervention (n=412)

TMS group

Allocated to intervention(n=444) Received allocated to intervention (n=444)

Randomized (n=856)

Analyzed (n=438)

Excluded (n=6) • Cancelled donation, n=2. • Embryo vitrified, n= 4.

Analyzed (n=405)

Excluded (n=7) • Endometrial bleeding, n=1. • Cancelled donation, n=2. • Embryos vitrified, n=4.

Assessed for eligilibility (n=930)

Follow-up (n=412) Follow-up (n=444)

Rubio I. et al. Fertil Steril 2014; 102: 1287-94

PÁG.16

TMS GROUP(n=438) CONTROL GROUP(n=404) p

Blastocyst rate (%) 27.5 24.5 NS

Embryo Fragmentation (%) 7.5 (7.2-7.9) 6.9 (6.5-7.1) 0.06

Number of Blastomeres 6.9 (6.8-6.9) 6.9 (6.8-7.0) NS

Optimal Embryos (D3) (%) 46.2 43.1 0.010

Blastocyst rate (%) 52.3 50.5 NS

Optimal Blastocyst (D5) (%) 20.9 16.6 0.001

Transferred embryos (per treatment) 1.86 (1.8-1.9) 1.86 (1.8-1.9) NS

Cryopreserved embryos (per treatment) 3.9 (3.6-4.1) 3.6 (3.4-3.9) NS

46.2 43.1 0.010

20.9 16.6 0.001

Rubio I. et al. Fertil Steril 2014; 102: 1287-94

PÁG.17

Pregnancy (%)

Ongoing pregnancy (%)

Positive ßHCG

Intention to treat All treated cycles All transfers

57.9

49.1

20

25

30

35

40

45

50

55

60

TMS (n=466) SI (n=464)

48.2

36.4

20

25

30

35

40

45

50

TMS (n=466) SI (n=464)

61.6 56.3

202530354045505560

TMS (n=440) SI (n=405)

51.4

41.7

20

25

30

35

40

45

50

55

TMS (n=440) SI (n=405)

54.5

45.3

20

25

30

35

40

45

50

55

60

TMS (n=415) SI (n=373)

65.3 61.1

20

30

40

50

60

TMS (n=415) SI (n=373)

Fetal Heart Beat

p = 0.007

p = 0.0003

p = 0.12

p = 0.005

p = 0.22

p = 0.01

Rubio I. et al. Fertil Steril 2014; 102: 1287-94

PÁG.18

16.6

25.8

0

5

10

15

20

25

30

TMS (n=271) SI (n=228)

All pregnancies

Early pregnancy loss: Positive ßhCG but no FHB

All transferred embryos

p = 0.01

44.9

37.1

20

25

30

35

40

45

50

TMS (n= 775) SI (n=699)

Implantation rate: # embryo sacs / # embryos transferred

Ear

ly p

regn

ancy

loss

(%)

Impl

anta

tion

rate

(%)

p = 0.02

Rubio I. et al. Fertil Steril 2014; 102: 1287-94

PÁG.19

Model effect values OR p value

Incubation TMS versus SI 1.41 (1.06-1.871) 0.017 Day of Transfer Day 5 versus Day 3 1.76 (1.22-2.52) 0.002 Oocyte source Autologous versus

Donation 0.83 (0.60-1.14) ns

Age years per year 0.99 (0.94-1.05)

ns

TMS versus SI 1.41 (1.06-1.871) 0.017

Rubio I. et al. Fertil Steril 2014; 102: 1287-94

PÁG.20

If all of the 6000 treatments in the conventional incubator had been carried out using Time-Lapse Incubator, we could have expected about 545 additional pregnancies.

Rubio I. et al. Fertil Steril 2014; 102: 1287-94

PÁG.21

Time-lapse data to predict blastocyst development

PÁG.22

Embryo temporal distribution to reach blastocyst stage.

PNF (h)

010203040506070

<22.6 22.7-24.3 24.4-26.3 >26.4

1stC (h)

010203040506070

<25.2 25.3-27.1 27.2-29.1 >29.1

2ndC(h)

01020304050607080

<37.6 37.7-40.1 40.2-43.3 >43.4

p<0.05 p<0.05

Time-lapse data to predict blastocyst development

PÁG.23

Embryo temporal distribution to reach expanded blastocyst stage.

p<0.05 p<0.05

PNF (h)

05

101520253035

<22.6 22.7-24.3 24.4-26.3 >26.4

1stC (h)

05

10152025303540

<25.2 25.3-27.1 27.2-29.1 >29.2

2ndC (h)

05

10152025303540

<37.6 37.7-40.1 40.2-43.3 >43.4

Time-lapse data to predict blastocyst development

PÁG.24 PÁG.24

P<0.001

N= 872

Time-lapse data to predict blastocyst development

PÁG.25 PÁG.25

P<0.001

N= 396

Optimal blastocyst

Time-lapse data to predict blastocyst development

PÁG.26

*

*

229 477 74 134 14

Time-lapse data to predict blastocyst development

PÁG.27

Blastocyst prediction

Tracks cell divisions

Calculates timing intervals

Feeds timings to the classification tree

Generates an automated prediction

2. Classification Tree • HIGH probability to form a blastocyst if cell

cycle markers are within range

• LOW probability to form a blastocyst if cell cycle markers are outside of range

1. Automated Cell Tracking Software:

Time-lapse data to predict blastocyst development

PÁG.28

Eeva. HIGHHIGH

LOWLOW

PÁG.28

MEDIUMMEDIUM

P2: 9 h 20 min ≤ P2 ≤ 11 h 28 min P3: 0 ≤ P3 ≤ 1 h 44 min

PÁG.29

EEVA category Blastocyst Rate (%)

Optimal Blastocyst Rate

(%) HIGH

(n=103) 77.7 27.2

MEDIUM (n=467)

56.3 19.3 LOW

(n=270) 49.6 17.4

HIgh High-Med Med-High Low

yes no

yes no no yes

cc2

9.33-11.47

s2

0-1.73h

s2

EEVA category Blastocyst Rate Optimal

Algorithm Results Blastocyst prediction (n=840)

PÁG.30

Algorithm Results KID (n=245 transferred embryos)

EEVA category Implantation (%)

HIGH (n=88)

45.5

MEDIUM (n=108)

31.7

LOW (n=49)

30.6

Eeva Morpho

HIgh High-Med Med-High Low

yes no

yes no no yes

cc2

9.33-11.47

s2

0-1.73h

s2

PÁG.31

# p<0.0001 **p<0.001 relative to Morphology only

• Specificity – measures false positives

• Significantly improved

in 3 out of 3 embryologists

• More consistent

embryo assessment using D3 morphology + Eeva information

Conaghan et al. Fertility & Sterility (2013)

Time-lapse data to predict blastocyst development

Time-lapse and COS

a-

GnR

H

an-G

nRH

hCG FS

H

FSH

N= 319 ICSI oocyte donation cycles N= 2132 embryos

CONCLUSIONS

Personalized Medicine is the next step in ART

Time-lapse is a good method of embryo selection: correlation with embryo quality, implantation, ongoing pregnancy rates and miscarriage.

Time-lapse increases ongoing pregnancy rates by 10% in RCTs

Time-lapse is helpful in the prediction of blastocyst development

Aknowledgements

Marcos Meseguer Irene Rubio

Carmen Rubio Daniela Galliano Manuel Munoz Carlos Simón