Please Note: Blog posts are not selected, edited or screened by Seeking Alpha editors.

How To Evaluate The Future NASH Market, A Method!, A Model!

|Includes: Allergan plc (AGN), CNAT, GALT, GILD, GLMD, GNFTF, ICPT, JNJ, MDGL, NVS, PFE
Summary

NASH treatments future market is very complex to estimate.

NASH population estimations are not obvious but consensual values are raising, publications after publications.

Rates of care are very different among countries.

NASH is a segmented marketshared between fibrosis grades of patients, comorbidities risks and treatment’starget.

I propose a global forecast market model that tries to include all parameters.

NASH basics

Non-alcoholic steatohepatitis or NASH as it is more commonly known is the next stage of non-alcoholic fatty liver disease or NAFLD and is characterized by liver damage and inflammation.

If left untreated, NASH can leads to advanced fibrosis, cirrhosis of the liver and ultimately hepatocellular carcinoma or HCC.

Patients with NASH are now characterized by their fibrosis grades, from F0 to F4, FO meaning non-fibrosis and F4, cirrhosis.

NASH is also associated with a lot of comorbidities like, T2 diabetes, obesity, cardio vascular risk, hypertension and hyperlipidemia.

Population and NASH prevalence’s.

In the proposed model, countries populations were extracted from the WHO database, the NAFLD prevalence of each country is extracted from the last publications, and when the value is not known, we apply the regional rate, or the closest country rate.

The NASH rate in NAFLD patients used in the proposed model is the one presented at the last NASH TAG conference 2018 in UTAH  (20%), the previous published was higher (24%).

 The F0 to F4 rates in NASH populations are extracted from the same presentation.

You can see here the list of the countries, populations and NAFLD rates used, in the table below.

Country

Total Population

NAFLD rate (est)

NASH rate (est)

NASH Patients (est)

Albania

2,935,146

23.71 %

4.74 %

133,192

Algeria

43,007,769

13.48 %

2.69 %

943,513

Argentina

45,516,881

16 %

3.2 %

1,263,973

Australia

22,945,760

28 %

5.6 %

1,252,818

Austria

8,655,693

23.71 %

4.74 %

420,424

Bahrain

1,486,111

31.79 %

6.35 %

88,594

Belgium

11,634,331

23.71 %

4.74 %

540,468

Bolivia (Plurinational State of)

11,548,297

30.45 %

6.09 %

542,613

Bosnia and Herzegovina

3,758,147

23.71 %

4.74 %

184,889

Brazil

215,997,014

30.45 %

6.09 %

11,964,696

Belize

397,880

30.45 %

6.09 %

18,772

Brunei Darussalam

450,478

27.37 %

5.47 %

22,364

Bulgaria

6,884,344

23.71 %

4.74 %

332,994

Cameroon

26,332,965

13.48 %

2.69 %

444,780

Canada

31,593,536

20 %

4 %

1,296,987

Chile

18,842,420

23 %

4.6 %

820,495

China

1,402,847,838

20 %

4 %

55,134,005

Taiwan

23,402,449

13 %

2.6 %

636,230

Colombia

50,228,928

26.62 %

5.32 %

2,383,353

Mayotte

273,172

13.48 %

2.69 %

4,819

Costa Rica

5,043,683

30.45 %

6.09 %

281,774

Croatia

4,162,499

23.71 %

4.74 %

200,424

Cyprus

1,218,234

31.79 %

6.35 %

76,917

Czech Republic

10,573,294

23.71 %

4.74 %

506,621

Denmark

5,775,634

23.71 %

4.74 %

270,505

Dominican Republic

11,106,596

30.45 %

6.09 %

545,141

Ecuador

17,338,395

24 %

4.8 %

681,007

El Salvador

6,230,899

30.45 %

6.09 %

322,282

Estonia

1,295,159

23.71 %

4.74 %

60,925

Finland

5,585,091

30 %

6 %

332,227

France

65,720,030

26.8 %

5.36 %

3,396,313

French Guiana

304,198

30.45 %

6.09 %

14,017

French Polynesia

274,919

31.79 %

6.35 %

15,478

State of Palestine

5,333,377

31.79 %

6.35 %

223,629

Germany

80,392,216

27.2 %

5.44 %

4,564,670

Greece

10,825,413

31 %

6.2 %

688,963

Grenada

109,387

30.45 %

6.09 %

5,615

Guadeloupe

477,509

30.45 %

6.09 %

26,880

Guam

167,559

31.79 %

6.35 %

9,968

Guatemala

18,014,921

30.45 %

6.09 %

784,076

Guyana

786,793

30.45 %

6.09 %

39,203

Haiti

11,378,336

30.45 %

6.09 %

520,889

Honduras

8,650,558

30.45 %

6.09 %

416,526

Hungary

9,684,938

22.6 %

4.52 %

445,588

Iceland

342,141

18 %

3.6 %

11,579

India

1,388,858,917

27.37 %

5.47 %

62,622,193

Indonesia

271,857,420

27.37 %

5.47 %

12,430,716

Ireland

4,874,292

23.71 %

4.74 %

213,064

Israel

8,718,236

31 %

6.2 %

448,010

Italy

59,741,327

22.58 %

4.51 %

2,797,762

Cote Ivoire

25,565,562

13.48 %

2.69 %

428,750

Japan

125,039,024

24 %

4.8 %

6,286,831

Jordan

8,166,792

31.79 %

6.35 %

379,131

Kenya

52,186,722

13.48 %

2.69 %

899,558

Republic of Korea

51,251,486

27.3 %

5.46 %

2,892,441

Lebanon

5,891,495

31.79 %

6.35 %

337,474

Latvia

1,918,949

23.71 %

4.74 %

91,633

Lithuania

2,794,898

23.71 %

4.74 %

133,828

Luxembourg

605,111

23.71 %

4.74 %

28,269

Madagascar

27,798,964

13.48 %

2.69 %

477,550

Mali

20,456,890

13.48 %

2.69 %

306,753

Malta

422,960

23.71 %

4.74 %

20,591

Mexico

134,837,046

26 %

5.2 %

5,911,541

Morocco

36,444,324

13.48 %

2.69 %

824,060

Oman

4,815,876

31.79 %

6.35 %

279,375

Netherlands

17,185,112

23.71 %

4.74 %

810,572

Curacao

163,757

30.45 %

6.09 %

9,523

Aruba

105,397

30.45 %

6.09 %

6,275

New Caledonia

258,499

31.79 %

6.35 %

15,923

Vanuatu

284,732

35 %

7 %

13,944

New Zealand

3,988,535

24.13 %

4.82 %

183,085

Nicaragua

6,417,990

30.45 %

6.09 %

317,678

Nigeria

206,830,983

8.67 %

1.73 %

2,160,692

Norway

5,493,603

23.71 %

4.74 %

251,346

Panama

4,230,971

30.45 %

6.09 %

217,777

Paraguay

7,067,097

24 %

4.8 %

271,554

Peru

33,317,111

13 %

2.6 %

722,862

Poland

38,407,266

23.71 %

4.74 %

1,847,867

Portugal

10,160,830

23.71 %

4.74 %

499,511

Puerto Rico

3,674,977

30.45 %

6.09 %

215,304

Qatar

2,452,180

31.79 %

6.35 %

157,708

Reunion

891,863

13.48 %

2.69 %

21,690

Russian Federation

142,898,124

23.71 %

4.74 %

6,600,746

Saint Lucia

191,765

30.45 %

6.09 %

10,540

Saint Vincent and the Grenadines

110,741

30.45 %

6.09 %

5,953

Saudi Arabia

34,366,240

31.79 %

6.35 %

1,817,574

Serbia

8,673,604

23.71 %

4.74 %

409,048

Slovenia

2,075,011

23.71 %

4.74 %

99,704

South Africa

56,668,602

13.48 %

2.69 %

1,233,742

Zimbabwe

17,470,705

13.48 %

2.69 %

300,083

Spain

46,193,543

25.8 %

5.16 %

2,436,785

Suriname

564,888

30.45 %

6.09 %

29,184

Swaziland

1,366,266

13.48 %

2.69 %

25,182

Sweden

10,120,396

20 %

4 %

393,176

Switzerland

8,654,271

23.71 %

4.74 %

415,822

Togo

8,293,638

13.48 %

2.69 %

141,181

United Arab Emirates

9,822,014

31.79 %

6.35 %

643,319

Tunisia

11,835,284

20 %

4 %

420,465

Turkey

82,255,782

35 %

7 %

5,023,192

Ukraine

43,679,300

23.71 %

4.74 %

2,076,642

TFYR Macedonia

2,088,374

23.71 %

4.74 %

97,603

Egypt

100,517,804

28 %

5.6 %

4,211,351

United Kingdom

66,700,126

26.4 %

5.28 %

3,409,809

Channel Islands

167,489

23.71 %

4.74 %

8,103

USA

273,745,471

26 %

5.2 %

13,654,677

United States Virgin Islands

107,015

30.45 %

6.09 %

6,118

Uruguay

3,279,412

30.45 %

6.09 %

196,156

Venezuela (Bolivarian Republic of)

30,331,990

25 %

5 %

1,203,399

NASH drugs candidates

 With no treatments to date, NASH is presented as the future big market by laboratories. A race started to propose the first treatment on the market and, to date, 50 to 60 drugs are on the list.

You’ll find below a list of drugs candidates with their name, laboratory and clinical trials phase in course, this list is not exhaustive and you can follow the link LIST to see the last update of it.

DRUG Candidate

Laboratoire

Phase

OCALIVA

INTERCEPT

3

ELAFIBRANOR

GENFIT

3

CENICRIVIROC

ALLERGAN

3

SELONSERTIB

GILEAD

3

GR_MD_02

GALECTIN

2

IMM124E

IMMURON

2

GS0976

GILEAD

2

ARAMCHOL

GALMED

2

EMRICASAN

CONATUS

2

MGL_3196

MADRIGAL

2

VOLIXIBAT

SHIRE

2

NGM282

NGMBioPHARM

2

GS9674

GILEAD

2

TROPIFEXOR

NOVARTIS

2

LMB763

NOVARTIS

2

MN_001

MEDICINOVA

2

BI_1467335

Boehringer_Ingelheim

2

MSDC_0602

CIRIUS_THERAPEUTICS

2

PF_05221304

PFITZER

2

DF102

AFIMMUNE

2

SAROGLITAZAR

ZYDUS

2

BMS986036

Bristol-MyersSquibb

2

LANIFIBRANOR

INVENTIVA

2

EDP_305

ENANTA

1

SEMAGLUTIDE

NOVONORDISK

2

SP_1373

SPITFIRE_PHARMA

0

NITAZOXANIDE

GENFIT

2

ISOSABUTATE

NORTHSEA_THERAPEUTICS

1

GRI_0621

GRIPharma

2

VK2809

VIKING

2

NALMEFENE

TaiwanJPharmaceuticals

2

LIK066

NOVARTIS

2

MT_3995

MitsubishiTanabePharma

2

NAMODENOSON

CANFITE

2

GEMCABENE

GEMPHIRE

1

FORALUMAB

TIZIANA

2

SOTAGLIFLOZIN

SANOFI

2

PF_06667272

PFITZER

1

PF_06865571

PFITZER

1

KBP_042

NORDICBIOSCIENCE

1

PF_06835919

PFITZER

1

DUR928

DURECT

1

NGM313

NGMBio

1

BMS_986171

Bristol-MyersSquibb

1

CER_209

CERENIS

1

NAMACIZUMAB

BIRDROCKBIO

1

ND_L02_s0201

NITTODENKO

1

RTU_1096

SUCAMPO

1

DRX_065

DeuteRx

1

IONIS_DGAT2Rx

IONISPHARMA

1

INT_767

INTERCEPT

1

NC_001

NAIAPHARMA

1

NV556

NeuroVivePharmaceuticalAB

0

HEPASTEM

PROMETHERA

0

VK0214

VIKING

0

RLBN1127

RELBURN

0

TGFTX4

GENFIT

0

RYI_018

CREATIVEBIOLAB

0

GKT_137831

GENKYOTEC

0

CB4209-CB4211

CohBar

0

JH_0920

JOYCEBiotech

0

VPR_423

VisionaryPharmaceuticals

0

NVP022

NeuroVivePharmaceuticalAB

0

Drugs do not target the same kind of patients, some are targeting the metabolic causes of the disease and others are targeting consequences.

The most advanced, already in phase 3, are OCALIVA (ICPT), ELAFIBRANOR (OTCPK:GNFTF), SELONSERTIB (GILD) and CENICRIVIROC (AGN).

To compute the proposed model, each drug candidate is included in a database and qualified regarding the publications.

We apply notations on the stage of fibrosis targeted

(a note from 0% to 100% for F0 to F4 segment is given to each drug).

A note is also given on other pathologies interactions and potential restrictions

  • Cardio Vascular Risk Patients.
  • Diabetic Patients.

This is done to take in account potential use restrictions that could impact the market segments of each drug.

When data’s on adverse effect are not known, we assume that there are no potential restrictions of drug use. It can change as soon new data’s are published.

Potential drug agreement date is estimated regarding:

  • Clinical trial advancement.
  • Drug status (Fast Track, Breakthrough therapy or potential Subpart H).

We also suppose that only a little part of patients under prescription change their drug each year.

The rate could be different from one drug to the other because of the adverse effects or infusion method. 

To manage that, we give a treatment compliance note to each drug as a supposed percentage of prescriptions reiterated from year to year (example, 90% mean that the drug would lost 10% of patients under prescription each year). Those data’s could be updated, as soon news would be published!

BACKBONE TREATMENT? OR COMPLEMENTARY TREATMENTS?

Discussing with labs and KOLs it appear clear now that the future of NASH treatment will rely on combinations of drugs. 

It is to early to talk of packaged combos, because they will need more clinical trials to evaluate the doses ratios valuable for the majority of patients and it will take time.

What we are talking about is combinations of prescriptions.

 The most admitted scheme of future NASH treatment is the combination of a backbone treatment targeting the metabolic causes of NASH and, if possible, the comorbidities of the disease (T2D, hyperlipidemia, etc. …), associated with a complementary treatment targeting mainly fibrosis.

Patients with mild fibrosis (F1 and maybe F2) could be treated with the backbone treatment only and most patients with advanced fibrosis like F3 and F4 (compensated cirrhosis) could be completed with a drug targeting specifically hepatic fibrosis. 

As more as the 'backbone treatment' will reduce inflammation and stop the fibro-genesis, the complementing treatment would be reduced. After a certain time (maybe 1 or 2 years), only the 'backbone treatment' would be maintained, maybe for the rest of the life of patients.

The average age of NASH patients is close to 55 years. It means that the 'backbone treatment' could be prescribed for an average duration of 20 years.

On the other hand, anti-fibrotic complementary treatments could be prescribed for an average duration of 2 years.

It means, that a 'backbone treatment' should present a perfect safety, and more than that, no adverse effects reducing the patient compliance to the treatment, when dedicated anti-fibrotic treatment can support some small adverse effects. 

Economy of health principles show that treatments supposed to be prescribed as backbone treatment and for lifetime need to have a low cost. At the opposite, short duration treatments can be more expensive. 

In the model proposed, each drug is tagged regarding the following criteria’s, allowing it to obtain the potential, 'backbone treatment' label:

  • Targeting metabolic causes of NASH (not targeting advanced fibrosis only)
  • A perfect safety as known to date.
  • No aggravation of comorbidities as CV risk or T2D
  • No adverse effects which could reduce the patient’s compliance to the treatment.
  • If possible, positive action on glycaemia and lipidemia.

The other drugs are classed as complementary treatment, as they mainly target fibrosis.

What is the purpose of the forecast?

What I intent to do, is to evaluate for the next 10 following years and country by country: 

  • The NASH population evolution,
  • The estimated percentage of this NASH population diagnosed and treated,
  • The estimated market share of each drug,
  • The estimated drug income generated by estimated sells.

It is a big challenge because it rely on many suppositions but I tried to be as conservative as possible defining all the parameters.

How the forecast figures are computed?

The market is divided in two big different segments that overlap because of possible combinations of drug prescriptions!

A first segment including only 'backbone treatments' of disease:

If they have low adverse effects, this kind of treatment compliance rate notation is quite high. It is known that backbone treatments prescriptions are rarely modified if the patient’s compliance to the treatment is good. 

So, as installed, those drugs benefit from a good market shield against new comers. 

A second segment including all the other drugs, named as ‘complementary drugs’:

Depending of side effects, the compliance rate of those drugs is a little bit lower.

Computations, market shares and figures are computed separately for those two segments!

  

Forecast computation basic's 

Market shares are computed on a large panel of patients from F1 to F4. 

Average prices are not estimated very high (from US$15000/year to US$1000/year depending of countries and type of treatment: backbone or not)

Treatment costs estimated are available country by country on this page:  CLICK TO SEE THE MAPS

Regarding the short period of treatment, the 'complementary drugs' market would be supposed to decrease as the backbone treatments are expanding, but because of the very low diagnosis rate of the disease, we supposed that the new diagnosed patients will compensate the end of 'complementary treatment' rate.

For complementary drug targeting fibrosis, market shares are computed on a reduced panel of patients from F2 to F4, depending of the drug profile.  

We divided the NASH patients into 20 groups:

2 large groups: CV risk / or not, subdivided into 2 subgroups: diabetics (T2) or not, each of these 4 subgroups being distributed according to their grade of Fibrosis (0 to 5)

Each group is named as follows: F (grade of fibrosis) (N) C (N) D

Example: for patients with diabetes without cardiovascular risk and a fibrosis grade of 2, the group is named F2NCD. (Sometime mentioned on graphs) 

Some drugs target only certain grade of fibrosis; others are poorly indicated for patients with cardiovascular risk, etc.

Using the notations already mentioned, every drug receive an individual note (0 to 1) for each subgroup of patients.

For each of these groups, a curve is calculated month by month to simulate the maturation of the market. Its purpose is to model the progression of the knowledge of the disease and the evolution of the diagnosis rate over time.

Curbs are different for each group because it is obvious that a diabetic patient with advanced fibrosis is more likely to be diagnosed quickly than a patient with a fibrosis grade of 1, non-diabetic and without identified CV risk.

A second progressive factor is calculated month by month for all the patients, it is the progression of the rate of care related to the management of the disease, and it starts from 0 and progresses linearly to reach its maximum rate in 15 years. The maximum rate of care is different for each country; it varies between 1% for a country like Sudan and 80% for Germany or France, and 70% for the USA. 

It is mainly representative of the country healthcare system and insurances covering.

As explained before, NAFLD total adult patients population is calculated for each country on the basis of known and published ratios.

Based on NAFLD population, a global NASH population is estimated on the basis of the (recently published) average ratio of 20% of NAFLD patients. 

This NASH population is then subdivided into subgroups using published ratios of diabetic rates, CV risks and each grade of fibrosis.

By multiplying the number of patients thus calculated by the first curve, group by group and then with the second curve, this time globally, one obtains, group by group and month by month the number of theoretical patient diagnosed and potentially treated. 

A population growth curve unique for each country is applied to the final, month by month.

The supposed treated patients will be then distributed among drugs regarding their future estimated market share.

Example: The evolution of NASH patients diagnosed and treated in USA, segment by segment. 

Initially, on each segment, every drug is compared with the other drugs to evaluate its market share and then an amount of patients is attributed to the drug. Then, for each drug, we take each month the number of patients treated on the previous month, but we remove a part of it, depending of the compliance rate. 

Those ‘new liberated' patients are added to the new amount of diagnosed patients of the month as previously computed and all those new patients are then dispatched to all the drugs, segment by segment.

At the end we obtain for each country, each drug, each group and each month, the estimated number of patient treated. This is a big amount of figures to compute, 100 countries x 50 drugs x 120 months x 20 groups give about twelve million figures.

Once the number of patients is calculated per segment, it is summoned to obtain the total of treated patient of all groups, per month, by country, and by drug. Then, the estimated treatment cost is fixed drug-by-drug and country-by-country 

The treatment cost evolution

It would be too simple to have a fixed cost for 10 years, so we evaluate month-by-month the number of drugs present on the market of each country. 

To simulate competition's impact on prices, when more than 4 drugs are present on the same segment, a discount rate is progressively applied to every drug prices in the country (to date, maximum discount is arbitrary limited to 35%).

Then, month-by-month, the price of each drug is multiplied with the previously calculated patients number of each drug, which gives us a forecast income, month-by-month, which is then presented by quarter or by year according to charts!

What the model results show us?

 

First, we get a very conservative ratio of patients diagnosed and treated.

As an example, for US Market, the estimated NASH patients treated by year are as follow:

In terms of percentage it mean:

Only 3% of the estimated NASH population would be treated by 2025

Second, despite the small ratio of patients treated, the market is huge and will grow very quickly.

Below you can see the worldwide forecast of the NASH market:

Conclusion

The NASH future market seems under evaluated, drugs managing to reach the market will become blockbusters in a few years, only because of the size of this market. Incomes over 1B$ could occur since 2022 for ICPT and OTCPK:GNFTF!

To my opinion, because of the global economic cost of treatment, the prices of NASH drugs could drop drastically as long as the diagnosis rates will prosper.

But to compensate, as Asia is under represented in the model in the first years, China could boost the market by 2028 2035.

Many figures are available on the dynamic pages of my blog and unfortunately it was not possible to include it directly in this article. All data’s presented here are also available as detailed interactive data’s on : www.nashbiotechs.com

Disclosure: I am/we are long GNFTF, GLMD, GALT.

Additional disclosure: I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.