
AsianScientist (Apr. 4, 2015) – I once went to a conference about global health governance, where a gentleman sitting beside me recounted his experience at a pharmacy in India. He had to purchase some drugs for his illness—an antibiotic and a painkiller. Fully expecting that he would end up paying a lot of money for the antibiotic (as they are typically more expensive), he was surprised that the pharmacist charged him only US$3 for the antibiotic and US$6 for the painkiller! Apparently, the pharmacy sold the locally manufactured version of the antibiotic, while they imported the painkiller from the US.
Such stories are commonplace in the developing world, where most of the population relies on generic drugs. In India, for example, branded generics make up 70 to 80 percent of the drug market. While generic versions go a long way in helping make drugs more affordable, the poor are still denied access to the latest life-saving drugs that science offers.
Gilead’s hepaptitis C drug Solvaldi currently sells at US$84,000 for a treatment course in the US. In a ground-breaking deal, Gilead has offered to sell Solvaldi at a staggering 99 percent discount in India. However, at US$900 or 54,000 rupees for a 12 week course, it still remains out of reach in a country where two thirds of the population lives on less than US$2 a day.
On big data and medicine
To begin to understand why drugs can be so expensive, we need to understand the economics of the process. Out of 5,000 to 10,000 newly discovered or synthesized drugs in the lab, only ten will reach the clinical trial stage. And out of the ten, on average only one of them will be approved by regulators. Hence, to develop just one approved drug, pharmaceutical companies need to fork out about US$1.8 billion in R&D costs over a span of 15-20 years, according to estimates published in Nature Reviews Drug Discovery. This is where big data and predictive analytics come in.
Most of us already encounter it on a daily basis: Amazon.com recommends us new things to shop for, and Google.com completes our sentences when we do a search. Quantitative systems pharmacology (QSP), as the field is known by, plays the same role in drug discovery and development. By integrating data from genetic sequencing, proteomic analyses, animal testing and human clinical trials, researchers can narrow down their drug candidates more efficiently.
In one such example, the clinical development pathway of the new drug PA-824 for tuberculosis was largely defined by an integrative analysis of preclinical findings. Another significant milestone in the real-world success of this field occurred in late January 2015, when the US Food and Drug Administration approved Natpara, a drug to control low blood calcium levels in patients with hypoparathyroidism. The dosing regimen of Natpara was determined using a systems pharmacology model that took into account the complex regulation of calcium in the body.
Who pays for the drugs?
While costs and uncertainty can be reduced by using big data and QSP approaches, approved drugs still face another hurdle: who pays for them? Most developing countries in the Asia Pacific region spend less than five percent of their GDP on healthcare, as compared to ten percent of GDP in the developed world.
This is where innovative financing mechanisms, such as Gavi, the Vaccine Alliance, come in. Mr. Paolo Sison, director for innovative finance and private sector partnerships at Gavi, explained that one of Gavi’s key goals is to reduce the large inequities in the vaccines market.
“We use long-term pledges from donor governments as backing for ‘vaccine bonds,’ which we then sell in the capital markets to have access to larger volumes of funding for Gavi programs,” he told Asian Scientist Magazine.
“Once the funding from donor governments, capital markets and the private sector are in place, Gavi is then able to negotiate and purchase vaccines on behalf of the world’s poorest countries. One purchase mechanism Gavi employs is the Advance Market Commitment (AMC) for pneumococcal vaccines.”
The pneumococcal AMC guarantees vaccine makers a predictable purchase price and volumes for vaccines that meet the needs of Gavi-eligible countries. To make this funding mechanism more sustainable, countries co-finance the vaccine payments with Gavi. Once the country of the purchasing government reaches a certain gross national income reshold, their co-payment share is increased gradually over a five-year period until they can pay for the vaccines on their own.
So far, Gavi has funded introductions of measles-rubella, pentavalent, and human papilloma virus vaccines in countries such as Cambodia, Indonesia and Laos. In December 2014, Gavi announced that it will commit to purchase Ebola vaccines for affected countries.
Hitting both sides of the profitability equation
In making the provision of drugs more equitable for everyone, we need to make drug development for developing countries a more lucrative endeavor. It is here that employing both big data and innovative financing can boost the profitability of this often neglected space in new drug development.
While their downstream effects are indeed promising, only with more time and concentrated effort can we fully demonstrate how these two tools can reduce the inequity in global health provision.
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